UNIQplus projects

AMES 01
Hymns and the history of Japanese popular music

Primary supervisor:

Dr Laurence Mann

Project description

This project is a key component of a bigger research project on the history of song in Japan. It aims to elucidate the relationship between hymn composition, adaptation and translation in the Meiji period in the broader context of regional, national and global trends in the consumption and of popular music during the late nineteenth century and into the era of early recorded music. Project members will investigate text settings, musical notation and early recordings of Japanese and Western hymns from this period to cast new light on the multimodal and intertextual relationships between them.

Project outcome

You will have the opportunity to gain experience working with contemporary Japanese and musicological sources, as well insight into the life of a postgraduate student in Asian Middle Eastern Studies, including access to specialist library resources.

Entry requirements

There are no specific degree requirements for this project. You should have EITHER (a) the ability to read music fluently or (b) a strong basic understanding of modern Japanese, including the ability to read hiragana. Basic IT literacy is also strongly preferred.
 

Anthropology 01
Co-parenting among the chaos: what we can learn from parents with toddlers during the UK lockdown

Primary supervisor

Dr Paula Sheppard

Project description

Biparental care is a defining feature of our species; women are able to produce and raise multiple dependent children because they have help from others: partners, grandparents, even friends. However, we also have strong cultures around the division of labour: in contemporary capitalist economies, at least one, but often both, parents work outside of the home and domestic labour (household chores), including childcare, has to be negotiated between them. In the UK, one of the reasons that people have very few children nowadays is because co-parenting duties tend to fall more heavily on the mother and this makes parenting and work-life more incompatible for women. This gender inequality in the family can lead to resentment and ultimately to avoiding having more children.

This study will use interview data collected during the UK lockdown (2020) from mothers and fathers of toddlers to explore how co-parenting practices were affected during this time, and what this may teach us about parenting challenges today. The data will be thematically analysed and written up into an article for publication.

Project outcome

You will be trained in qualitative methods such as thematic and content analysis and will learn to use NVivo software. You will also gain experience working within a research team which requires good communication skills, responsibility, and time-keeping. You will be co-authors on the main output; an original research paper to be published in a peer-reviewed social science journal, which will be an excellent addition to the CV of an aspirant masters or doctoral student.

Entry requirements

There are no specific degree requirements for this project. You should have an interest in evolutionary anthropology, sociology, qualitative demography, or a similar social science discipline. Good attention to detail and an ability to learn new skills independently are essential for this project.

Digital Humanities 01
Slave Trade and Plantation Management: The Barham Papers (1792-1820)

Primary supervisor

Professor Nicole Pohl

Project description

You will work with the Barham papers in the Bodleian Special Collections, containing, amongst others, the papers of Joseph Foster Barham I (1729-1789) and Joseph Foster Barham II (1759-1832). The family owned a considerable amount of plantations in the West Indies.

The papers, hitherto only rudimentary catalogued, document the management of these estates including the considerable amount of slaves. You will continue the inventory, started by a previous intern, write a blog, and prepare a mini-edition on a selection of letters to make slave ownership visible in the Bodleian Archives.

Project outcome

You will be trained in reading eighteenth-century manuscripts, transcribing eighteenth-century handwriting and introduced to editing/annotating manuscript letters. The output is a mini-edition of chosen letters to be published in EE.

You will learn how to compile a descriptive inventory of the manuscripts that will be used in the Bodleian Special Collection catalogue, with training in the conversion of catalogue information into standardised metadata, and an introduction to MARCO, the cataloguing system which underpins Bodleian Archives and Manuscripts.

You have an opportunity to write a blog about some of these letters and reflect on the challenges to make slavery visible in archives such as Electronic Enlightenment.

Entry requirements

You should have, or be studying a degree in English Literature, History, Digital Humanities, or a relevant discipline. No programming skills or prior knowledge of XML editing is required, these will be taught as part of the programme, but a basic computer literacy, and ability to work with simple data management software like Excel, is required. We would also ideally like the applicant to have downloaded and had some experience of plain text editing in an advanced plain text editor such as TextWrangler, Notepad++ or BBEdit.

Education 01
The teaching of poetry in secondary schools

Primary supervisor

Professor Velda Elliott

Project description

This is a project which aims to replicate and extend a study which was previously conducted in 1980 and 1999. We will have local data from Oxfordshire schools on poetry teaching 11-16, which is the replication part. Other teams around the world will be conducting parallel studies in their own countries, which is the extension part. Data generation is via a survey which will be open in the first few months of 2026. You will work with the research team for analysis and writing up.

Project outcome

You will have the opportunity to gain experience with quantitative and qualitative survey data. You will be mentored in and gain experience using thematic analysis of qualitative data and ways in which reliability can be considered in qualitative research. You will gain some experience of drafting journal articles for publication. You will be named on any publications which come out of the study. You may also gain some experience collaborating with an international team and in the process of writing book proposals.

Entry requirements

You should have, or be studying, a Social Science or English degree with an interest in education as an area of study. Experience with simple statistics would be advantageous. An interest in poetry reading or writing would also be advantageous.

Education 02
Negotiating shifting practices to preserve professional learning: the problem of planning

Primary supervisor

Dr Katharine Burn

Project description

This project seeks to understand how the process of lesson planning is changing in secondary schools. We are investigating how competing pressures of different kinds (including teacher shortage and workload issues, along with the promotion of AI as a time-saving strategy) have resulted in widespread centralised planning - with many schools using prescribed ‘booklets’, which dictate the format of every lesson and greater reliance by some teachers on Generative AI as a source of plans and teaching resources. We are examining the impact of these processes on teachers’ practice and on their sense of professional autonomy and agency. We are also reviewing the implications for initial teacher education programmes, in which learning to plan has traditionally served as a highly effective strategy in learning to teach. The research includes an interview-based case-study with multiple stake-holders, followed by a national survey.

Project outcome

You will join our friendly, collaborative team in time for a project workshop to which all the interview participants from the first phase will be invited (headteachers and senior school leaders, heads of department, subject mentors and student teachers). Here we will present findings from the qualitative phase of the study, and you will help to facilitate collective discussion of the preliminary findings and to gather participants’ insights into the key implications for their own practice and for the design of future teacher education programmes (including Oxford’s own PGCE course). You will also help to promote the national survey, drafting blog posts and other social media messages to encourage wide participation.

You will be given training to undertake analysis of some of the survey findings and will have the opportunity to prepare them for presentation. You may also be asked to conduct a literature search as the project team begins to build on this research with a further grant application. You will thus gain key insights into various stages of the research process, gaining important skills in research design, data collection and management and quantitative analysis. If aspects of your work are included in a future publication, you may be included as a co-author of that paper.

Entry requirements

You should have, or be studying for, a degree within the Humanities or Social Sciences. The project may be of particular interest to those studying Education or a related discipline, but since this phase of the research focuses on history teaching, experience of studying history at A-level or within your undergraduate degree would also be advantageous. You should have excellent organisation skills and be able to work independently. Some background in quantitative research methods is desirable but not essential.

English 01
Knotted Histories: Early modern global carpets, global exchange and the public country house

Primary supervisor

Professor Nandini Das

Project description

This project will involve working with the team on the 'Knotted Histories: Early Modern Global Carpets, Global Exchange and the Public Country House’ project, run between the Faculty of English at Oxford and the National Trust. This project aims to reconsider cultural and global contexts of early modern carpet production and their use beyond traditional approaches, focusing on craft makers and manufacturers alongside an examination of the cultural meaning of carpets.

As part of this, the project is focusing on a series of National Trust properties as Case Studies, considering both their historic and current carpet collections and the role carpets played in social spaces within these properties. This study is conducted through archival analysis, examining catalogues, wills, inventories, and other documentation as appropriate, as well as through material analysis, considering the physical qualities and designs of the carpets.

Project outcome

The main task on this project will be producing a set of Case Studies for a selection of National Trust properties, modelled on the project’s existing Case Studies. Each of these Case Studies will include a historical overview of the property’s history, details on its existing carpet collections, analysis of its previous holdings, and material analysis of the carpets where possible. These Case Studies will also contain lists of possible archival sources identifying potential leads relevant for future work on the project.

Through this internship, you will have an opportunity to work with an interdisciplinary team engaged in work on material history, public engagement, early modern global networks, and wider questions of living and lived heritage. You will also gain experience working with heritage collections and considering some of the challenges facing heritage institutions. You will also gain experience working with special collections and manuscript materials, an insight into research libraries and archives, and experience working with humanities data.

At the end of the project, you will present your research to the project team in an internal meeting, and you will be credited if your material is used in future work. Supervisors will also assist you to write up any material you are particularly interested in for a post on the TIDE @ Oxford blog to be published at the end of the internship.

Entry requirements

There are no specific degree requirements for this project. You should be able to work independently, have basic IT skills and familiarity with Excel. You should have in interest in early modern history, art or literature, material history and working with heritage institutions. Experience in early modern palaeography, in particular secretary hand, would be advantageous but not essential as training can be provided.

Linguistics 01
Social interaction and speech development in primary school children in London

Primary supervisor

Professor Devyani Sharma

Project description

In their early years (ages 4-8), children develop a range of speaking styles to navigate the world, which have a central role in their later life outcomes. In a mixed East London school, children may start out with similar language but quickly develop accents that reflect their social class and ethnic background, despite sharing the same wider peer group and input. At what age do they start avoiding or adopting speech features for social reasons? Can we observe this growing awareness in the speech they use in work and play during their early childhood years?

As part of the Generations of London English project, we collected recordings of children aged 4-8 at two time points per year, to track how their language develops. The recordings include word naming, dialogue tasks, and free play. We will be transcribing these social interactions, extracting features of accent, syntax, and discourse, and performing quantitative and social interactional analysis of their speech.

Project outcome

You will be trained in the use of cutting-edge AI and transcription tools (WhisperAI; ELAN) and speech analysis software (Praat; Montreal Forced Aligner). You will also be trained to handle complex data, annotating and tracking features of accent, grammar, and conversation/interactional structure. Your guided reading will introduce you to theories of child language development, speech style, and the structure of spoken social interaction. You will learn about larger theoretical debates and can observe how a large research project works. At the end of the internship, you will have the opportunity to author a blog post and/or a brief video to be used by A-Level teachers and students (www.teachrealenglish.org).

Entry requirements

You should have, or be studying, a degree in Linguistics or a related discipline. You should be comfortable using MS Word and Excel. Knowledge of Praat, ELAN, and/or WhisperAI is not necessary but is useful. Familiarity with the International Phonetic Alphabet (IPA) is advantageous. You should be a self-starter with the ability to work independently.

Geography 01
Measuring recovery of post-agricultural temperate forests

Primary supervisor

Professor David Moreno Mateos

Project description

We aim to understand how forests recover their complexity after the end of agriculture. This is a rapidly growing trend globally that will only increase in the coming decades as agriculture intensifies.

You will be involved in soil and plant sample collection in the field and in processing those and other samples in the lab for their analysis. This includes soil samples preparation from chemical analysis or DNA extraction and purification for genomic analysis. Understanding the recovery process will help us design more efficient tools to recovery the complexity of restored forests.

Project outcome

You will learn to design field sample collections in response to specific research questions. You will work with those samples in a lab and prepare them for external analysis in specialized facilities. This will help you to have a first interaction with molecular biology techniques and how they can be used to improve current conservation and restoration efforts.

You will have the opportunity to engage with several research groups in the School of Geography and the Environment and potentially the Department of Biology in talks, lab meetings and discussion groups with other undergraduate students, master’s students, DPhil students, postdoctoral researchers and faculty staff. You will have a chance to discuss the potential statistical approaches to use with the data you have generated and will have the opportunity of participating in publications derived from this research.

Entry requirements

You should have, or be studying, a degree in Environmental Science or equivalent (such as Biology or Physical Geography). Previous experience in molecular biology techniques would be advantageous.

Geography 02
Cultures of care, choice, and challenge: Exploring student and early career researcher expectations and experiences of animal use in science

Primary supervisor

Professor Beth Greenhough

Project description

A recent report by the RSPCA's Animals in Science Department (authored by those on the supervisory team) found that some UK students and early career researchers (ECRs) working with animal models experience limited discussion around alternatives to animal use and insufficient support for the emotional and ethical challenges that animal use can raise.

Informing realistic expectations for projects involving animal use, enabling flexibility around model choice, and providing a supportive environment are crucial for empowering emerging researchers to adopt non-animal methods (NAMs) and constructively engage with the challenges of animal use.

This qualitative social science project aims to develop further insights into how students and ECRs 'come into' animal work, their expectations, sense of choice and agency, and experiences of support. To do so, it will involve an interview study with ~10 students and ECRs who are currently, or have recently, engaged in research involving the use of animals.

Project outcome

You will gain experience and skills in reviewing academic literature, conducting in-depth semi-structured interviews, transcription, and undertaking thematic analysis. As the project is co-supervised by the RSPCA's Animals in Science Department, this project will provide an opportunity to work with the world's oldest animal welfare charity and gain experience of communicating research findings across sectors. If two students across the social sciences and biosciences are hosted, then you will also benefit from cross-disciplinary working. At the end of the project, you will have an opportunity to present your analysis to the research team and co-author a report and/or blog post, adding to the literature base on this important topic.

Entry requirements

You should have, or be studying, a social science-related degree, such as Sociology, Anthropology, or Human Geography, or a bioscience-related degree with an interest in the social and ethical aspects of scientific practice. You will be open-minded and able to work sensitively on complex and controversial topics. Understanding and experience of qualitative social scientific research is desirable but not essential.

Geography 03
Exploring the land surface response to global warming in high resolution convection permitting models using simplified theoretical models.

Primary supervisor

Dr Jerry B. Samuel

Project description

Soil moisture is an important parameter in land-atmosphere interactions. However, complex land surface models have challenges in accurately simulating soil moisture and the associated water and energy fluxes across the land surface. This contributes to uncertainties in projected changes in land-atmosphere interactions under global warming.

High resolution convection permitting models, capable of resolving processes that were hitherto parametrized, are expected to produce much better simulations of the climate system. These models predict delayed onset of monsoon rains, more dry days, and possibly more intense droughts, despite larger seasonal mean rainfall in various monsoonal regions under global warming.

This project will use a simplified energy balance approach to identify prominent processes that influence changing land surface characteristics during the monsoon season in these state-of-the-art simulations, with an aim to advance the understanding of the major drivers of future droughts.

Project outcome

You will join the Climate Research Lab to work closely with a team analysing climate observations and models and will:

• gain data analysis skills in Python;
• gain knowledge of the latest cutting-edge atmospheric models;
• gain skills working as part of a research team; and
• gain scientific communication and presentation skills.

It is possible that your contributions will lead to a short paper/conference presentation communicating the outcome of your internship research.

Entry requirements

You should have, or be studying, a degree in a relevant field, such as Physics, Earth Science, or Geography, although we would welcome students in Mathematics or Computer Science with an interest in weather and climate. You should have basic familiarity with software coding, such as Python or similar. You should be interested in atmospheric and climate science, but in-depth prior knowledge is not required.

Geography 04
Exploring observations and simulations of high-impact storms in Africa

Primary supervisor

Dr Francesca Morris

Project description

Mesoscale convective systems (MCSs) are thunderstorms which can last for hours or even days, and span hundreds of kilometres. Not only are they high-impact weather systems which contribute extreme winds and rainfall, but they can also modulate atmospheric circulations at scales beyond their own structures.

Few observations of MCSs in Africa exist, but the Oxford Climate Lab have recently led several field campaigns in Southern and Eastern Africa, during which MCSs passed through the field sites. Observations were regularly taken using radiosondes and lidar, and could provide a unique insight into the winds associated MCSs, and therefore their upscale effects on the atmosphere. This project will explore these observations and assess their potential to contribute to our understanding of the winds associated with MCSs, comparing these to state-of-the-art ensemble weather forecasts from the UK Met Office.

Project outcome

You will join the School of Geography and the Environment’s Climate Lab, a team of researchers studying weather and climate and specialising in the Southern Hemisphere. By the end of the project, you will have:

1. learned about global and regional meteorology and climate science;
2. learned how to analyse in-situ measurements from meteorological field campaigns;
3. gained understanding of numerical weather prediction models and their output;
4. gained advanced data analysis skills in Python, particularly for analysing geospatial data;
5. gained practical knowledge of applying advanced statistical techniques;
6. gained scientific communication and presentation skills; and
7. contributed to a short paper communicating the outcome of your internship research.

Entry requirements

You should have, or be studying for, a degree in a relevant discipline, such as Physics, Earth Science, or Geography. We would also welcome applicants with a background in Mathematics or Computer Science with an interest in weather and climate. Familiarity with basic programming in Python (or a similar language) and the ability to use a basic command line interface for computing is highly desirable, however, training in basic computing will be provided. An interest in atmospheric and climate science is helpful, but in-depth prior knowledge is not required.

Geography 05
Understanding the dry-to-wet transition period in winter rainfall dominant regions

Primary supervisor

Dr Marcia Zilli

Project description

Rainfall onset has been a major focus in tropical monsoon regions of the world. Less focus has been given to the start and/or increase of rainfall in temperate regions with warm drier summers and cooler wet winters. This delay in the start of soil moisture accumulation has major implications for planting dates, yields and crop survival. In south-eastern Australia, previous studies reported a delayed autumn rainfall (Pook et al., 2009), with large impacts in the wheat production. Similar delays are also observed in winter rainfall dominant regions in Southern Africa.

This project will use an onset detection algorithm to identify changes in the dry-to-wet transition period over these regions. Besides identifying the onset period, the algorithm also estimates the amount of rainfall and dry days during the transition, providing crucial information about the water cycle. You will apply this algorithm to regionally relevant observational and model datasets and identify changes in the timing and rainfall characteristics of the wet season onset.

Project outcome

You will join the Climate Research Lab to work closely with a team developing python software to analyse climate observations and models. By the end of the project you will:

• gain experience in using climate datasets (both observational and modelled);
• gain an understanding of trends and relevant statistics and plots to communicate them;
• develop skills working as part of a research team; and
• gain scientific communication and presentation skills.

It is possible that your contributions will lead to a short paper/conference communicating the outcome of your internship research.

Entry requirements

You should have, or be studying, a degree in a relevant discipline, such as Physics, Earth Science, or Geography, although we would welcome students with a background in Mathematics or Computer Science with an interest in weather and climate. You should have basic familiarity with software coding, such as Python or similar. Experience using netCDF files and Xarray in Python is highly desirable. In general, you should be keenly interested in atmospheric and climate science - but in-depth prior knowledge is not required.

Geography 06
Winds of change: Low-level jets and the future of monsoons

Primary supervisor

Dr Callum Munday

Project description

Over two thirds of the world’s population live in monsoon regions, where climate change is projected to have large impacts on the timing and characteristics of precipitation. These changes can have significant impacts on rain-fed agriculture and hydroelectric power which are often vital for food and electricity production in these regions. Therefore, improving our understanding of monsoons is essential for mitigating the damage caused by climate change.

One key component of the Indian and African monsoons are low-level jets which are key sources of moisture and an important energy flux within and between the hemispheres. Climate models tend to poorly represent these low-level jets due to their complex interactions with topography. Therefore, they may be a key component in explaining the wide variation in future projections between models. This project will use simulations of the future climate to explore the relationships between low-level jets and monsoons to constrain model projections.

Project outcome

The relationship between low-level jets and monsoons is likely a key piece of the puzzle when considering future trends in these regions. Therefore, this project will contribute to novel research which may form part of a published academic article in the future. In addition to the academic contribution, you will gain several skills which are applicable to both future research in meteorology/climate and other research and non-research areas. Working with global climate datasets will allow you to develop your coding skills including using python and CDO to produce cartographical and graphical representations of spatial data. You will also develop an understanding of regional and global meteorology and climate and will explore how future climate projections, such as those produced by the IPCC, are created. In addition to this, there will be various opportunities to practice presentation skills in front of the wider Climate Lab research group and to take feedback on your work.

Entry requirements

You should have, or be studying, a degree in geography, earth sciences, physics, mathematics, computer science, or a related discipline. Key to this project is enthusiasm about atmospheric and climate science, but previous knowledge is not required. Prior experience programming using Python would be beneficial but is also not essential as basic training will be provided.

History 01
Oral history and lived experience in mind brain health

Primary supervisor

Dr Sloan Mahone

Project description

Epilepsy affects more than 50 million people worldwide, with a disproportionate burden in low-to-middle income countries where the effects of epilepsy-related stigma are the most severe. This project will contribute to a well-developed Embedded Oral History Project that incorporates the lived experiences of people living with epilepsy into research, advocacy, and education. We are currently developing The Oral History Project and Repository as part of the Centre for Global Epilepsy.

You will work with multiple disciplines across the Humanities and Global Health to carry out historical and/or global health research, health communications and advocacy materials, and the development of social media or research content in epilepsy or Mind Brain Health. Projects will consider individual interests and may be explicitly historical in nature or may work across Humanities and Health related disciplines.

Project outcome

You will work both independently and as part of a larger team. We will consider your specific interests in project design but core experience and skills may include: advanced literature review, archival research, database organization, oral history analysis, website and communications development, writing for a broad audience, and presenting to a group.

Entry requirements

You should have, or be studying, a humanities degree, with a strong preference for experience in History. You should have a strong interest in at least one of the following: global health, mental health, history of medicine or science, digital humanities, or neuroscience and the humanities. An interest in regions of the world outside of Europe (particularly Africa, India, Brazil) is beneficial. Strong writing and communications skills are essential as is strong computer literacy. You will be expected to work both independently and as part of a very engaged and friendly team.

History 02
Conceptions of parenthood in the late nineteenth century

Primary supervisor

Dr Christina de Bellaigue

Project description

This project would explore the different conceptions of parenthood articulated in the journal of the Parents' National Education Union - the Parents' Review - edited by Charlotte Mason, in the period 1880-1920 in order to examine how ideas of motherhood and fatherhood evolved over the period, and to trace the impact of new pedagogical ideas, changing ideas of gender, and the extension of schooling before and through the Great War.

Project outcome

You will learn how to develop a historical research project based on the analysis of media and periodical literature, gain experience of sampling techniques and keyword searches, and have the experience of working in special collections as well as gaining insight into research libraries and postgraduate work in history; you will gain knowledge of the historiography of childhood and parenthood in the period. At the end of the period you may have the opportunity to write a blog post published on the Oxford Centre for the History of Childhood website. Aspects of your analysis may be included in a future publication, at which point you will be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a degree in History. An interest in modern social and cultural history, and the history of the family, would be advantageous. You will be a self-starter with the ability to work independently.

History 03
The Newton Project

Primary supervisor

Professor Rob Iliffe

Project description

You will learn basic information relating to the life and work of Isaac Newton, and more generally in the history of science. You will work with leading historians and practitioners of digital humanities, and will receive training in the use of AI-enhanced analytic tools for creating data (including but not limited to transcribing handwriting and printed materials), both contributing to and analysing the large dataset comprising the Newton Project.

This project is aiming to create an Open Access digital edition of all of Newton's published and unpublished writings by 2030. You will gain knowledge both of Newton and his ideas and also of state-of-the-art historical research into these topics. You will also be introduced to key techniques and approaches in the use of AI in modern humanities research, thereby acquiring key transferable skills.

Project outcome

You will gain knowledge of Newton, his ideas and also of state-of-the-art historical research into these topics. You will also be introduced to key techniques and approaches in the use of AI in modern humanities research (eg using Handwriting Recognition Software and the development of LLMs), thereby acquiring key transferable skills.

Entry requirements

There are no specific degree requirements for this project, however, a History or Computer Science background would be advantageous. Ideally, you will be familiar with Python and other relevant computing skills, however, further training (such as Handwriting Text Recognition training) can be provided. You should be interested in Isaac Newton and his works, as well as in ways by which significant scientific materials can be made available online using AI-powered approaches. Further information can be found on the Newton Project websites (https://cudl.lib.cam.ac.uk/collections/newton/1, https://www.newtonproject.ox.ac.uk/ and https://newtonandthemint.history.ox.ac.uk/).

History 04
Religion and the life-cycle in Elizabethan England

Primary supervisor

Professor Lucy Wooding

Project description

The history of Reformation religion in England is frequently studied separately from the social history of the period. This project seeks to bring together the experiences of birth, marriage, and death with the religious faith and practice which contemporaries used when going through these rites of passage in an age of religious transition. You will work in one of these three areas (birth, marriage, and death), examining a range of sources including treatises, homilies, diaries, letters, plays and poetry. You will be asked to formulate ideas about how early modern society understood these life experiences within a religious framework.

Project outcome

You will acquire experience working with a range of early modern sources, in both printed and manuscript form. You will gain a good working knowledge of historical debates concerning both Reformation history and the social history of early modern England, with particular attention to the gendered elements of the latter. You will also have the opportunity to formulate your own evidence-based ideas about how the history of religion should be approached. At the end of the project you will present your findings to a group of researchers in this area.

Entry requirements

You should have, or be studying, a degree in History. Some previous knowledge of early modern history would be an advantage. You should be a highly-motivated student with an interest in pursuing historical research.

Politics 01
Conservatism in the Atlantic World in the Nineteenth Century

Primary supervisor

Professor Adam Smith

Project description

You will work on the Leverhulme Trust-funded project "Conservatism in an Age of Revolution", which attempts to map and explain the emergence and spread of the language of conservatism in the US, Britain, Latin America and other countries in the period between about 1830 and 1900. You will be given a specific set of sources (in English, or, if you have reading knowledge in Spanish, French or German), such as a run of newspapers or magazines, and asked to map out the use of some key political terms, noting their context and offering some thoughts about their use and how they changed over time.

Project outcome

You will have the opportunity to work with digitised or real-life primary source material from the nineteenth century, and will gain knowledge and understanding of the methodological challenges involved in tracing the use of political language. You will have the opportunity to write a blog post and/or an article in the RAI's Annual Report. The research you complete will contribute to publications and your contributions will be fully recognised and acknowledged. You will be part of a project team and gain understanding of how collaborative work can effectively be done.

Entry requirements

You should have, or be studying, a degree in History. Language skills would be advantageous, however, are not a requirement as the research could be conducted in English.

Internet Institute 01
The digital lives of care-experienced children

Primary supervisor

Professor Ekaterina Hertog

Project description

This project explores the digital lives of care-experienced children, focusing on how policies, services, and existing research shape their opportunities to build digital skills and confidence. You will support the knowledge-mapping phase, which brings together two strands of work.

First, a scoping review of academic and grey literature will be conducted using established frameworks to identify what is currently known about digital inclusion, access, risks, and literacy for care-experienced young people. Second, a policy-mapping exercise will examine how local authorities in England support the development of digital capital, highlighting variations in provision and emerging promising practices. Together, this phase aims to build a clear picture of the structural and policy landscape, identifying gaps that will guide later stages of the research.

Project outcome

You will gain practical experience in conducting a systematic literature review, including designing search strategies, screening literature, and synthesising findings across academic and grey sources. You will also develop skills in policy mapping and comparative analysis across local authorities. Through this work, you will build a strong understanding of current evidence and gaps concerning the digital lives of care-experienced children, enabling you to discuss the strengths, limitations, and policy implications of existing knowledge.

Entry requirements

You should have, or be studying, a degree in a relevant social science discipline, such as Economics, Political Science, Sociology, Social Psychology, or Anthropology. Experience with doing knowledge mapping (e.g. in a form of systematic literature review, a scoping review etc) would be very advantageous.

Internet Institute 02
Public interest technology research on AI social biases

Primary supervisor

Professor Luc Rocher

Project description

AI systems often appear neutral but still reproduce prejudice and biases tied to gender, race, age, disability, or socioeconomic status. These effects can emerge in language models, predictive classifiers, recommender systems, privacy techniques designed to safeguard data, or surveillance frameworks used to monitor behaviour. They can lead to real and significant harms by shaping how people are represented and treated.

This project broadly investigates how such technologies encode and amplify social biases and how these dynamics feed back into real peoples’ experiences. The scope is deliberately open: you can examine representational patterns in LLM outputs, measure performance drops in classification systems, study how privacy technologies alter the visibility of minority groups in data, or explore the impact of mass surveillance technologies.

Depending on your interests, you might design a new evaluation method, help us conduct a study with human participants to measure real-world effects, or analyse large existing datasets to identify systematic disparities. You could also work on building an interactive web tool to convey the risks/benefits of using Machine Learning systems to the general public.

Project outcome

You will gain experience analysing the social impact of ML-based systems. You will learn to construct targeted evaluations, interpret model and system behaviour, and/or design empirical studies involving human participants. You will strengthen your programming and data analysis skills.

Entry requirements

You should have, or be studying, a degree in Computer Science, Artificial Intelligence, or a related STEM discipline. A solid understanding of machine learning concepts is essential, along with proficiency in programming languages such as Python. Experience with data analysis and visualisation tools will also be advantageous. Successful candidates will be working on real research projects and so must be self-motivated and fit into a team of researchers from various backgrounds. Good communication skills are important, as you will be expected to present findings clearly to the research team.

Law 01
Monitoring immigration detention

Primary supervisor

Professor Mary Bosworth

Project description

This project has two inter-related parts - one is to work with the Border Criminologies research team headed by Professor Mary Bosworth at the Centre for Criminology on communications, and the other is to work with our partner organisation the Association for Visitors to Immigration Detention (AVID) to produce a report on the role of volunteer visitor groups to immigration detention centres in the UK in building an evidence base on the day-to-day impacts and conditions in immigration detention. How does the work of these visitor groups relate to other monitoring mechanisms and advocacy efforts from partners against detention? What unutilised opportunities are there for further advocacy to build the case for the systemic harm of detention and ultimately the need to end detention?

Project outcome

You will be taught how to communicate academic research to a public audience, and how to analyse primary source evidence for policy development in the third sector. You will also be taught how to code and how to use qualitative research software. The report for AVID will involve analysing over 180 pages of minutes stretching over the past five years, to identify themes and issues. That material will need to be placed in the context of the human rights monitoring regime for sites of detention and in regards to these sites of confinement. You will also produce a short blog on this work for Border Criminologies. The Border Criminologies website work will involve social media training on Bluesky and Instagram as well as Canva. You will be guided through how to manage the documents for analysis, and will be taught the basics about the immigration detention system and human rights monitoring. This role will teach you transferable skills about communication and dissemination, which you could use in a variety of other posts.

Entry requirements

You should have, or be studying, a degree in Law or any of the social sciences. The requirements are open although some familiarity with immigration matters in the UK would be advantageous. Legal training would also be helpful. Familiarity with social media and managing online content would be beneficial. Any experience with thematic analysis and report writing would also be advantageous.

Linguistics 01
Social interaction and speech development in primary school children in London

Primary supervisor

Professor Devyani Sharma

Project description

In their early years (ages 4-8), children develop a range of speaking styles to navigate the world, which have a central role in their later life outcomes. In a mixed East London school, children may start out with similar language but quickly develop accents that reflect their social class and ethnic background, despite sharing the same wider peer group and input. At what age do they start avoiding or adopting speech features for social reasons? Can we observe this growing awareness in the speech they use in work and play during their early childhood years?

As part of the Generations of London English project, we collected recordings of children aged 4-8 at two time points per year, to track how their language develops. The recordings include word naming, dialogue tasks, and free play. We will be transcribing these social interactions, extracting features of accent, syntax, and discourse, and performing quantitative and social interactional analysis of their speech.

Project outcome

You will be trained in the use of cutting-edge AI and transcription tools (WhisperAI; ELAN) and speech analysis software (Praat; Montreal Forced Aligner). You will also be trained to handle complex data, annotating and tracking features of accent, grammar, and conversation/interactional structure. Your guided reading will introduce you to theories of child language development, speech style, and the structure of spoken social interaction. You will learn about larger theoretical debates and can observe how a large research project works. At the end of the internship, you will have the opportunity to author a blog post and/or a brief video to be used by A-Level teachers and students (www.teachrealenglish.org).

Entry requirements

You should have, or be studying, a degree in Linguistics or a related discipline. You should be comfortable using MS Word and Excel. Knowledge of Praat, ELAN, and/or WhisperAI is not necessary but is useful. Familiarity with the International Phonetic Alphabet (IPA) is advantageous. You should be a self-starter with the ability to work independently.

Modern Languages 01
Marie-Antoinette's Letters

Primary supervisor

Professor Catriona Seth

Project description

The Lettres de Marie-Antoinette (LMA) project brings together researchers from the University of Oxford and the Centre de recherche du château de Versailles, as well as from universities and archives across France, Austria and Sweden. The LMA project aims to identify and catalogue all surviving correspondence by Marie Antoinette. Until now, readers seeking to discover France’s most famous Queen through her own words have not had access to a complete, scholarly edition of her letters and have often had to confront the problem of counterfeit documents.

You will join members of the LMA team as the project moves to the exciting stage of transcribing the letters and preparing them for publication in accordance with modern academic standards. By making use of collaborative digital resources, which you will be trained to use, you will take part in the preparation of an edition which will be of use to future researchers.

Project outcome

You will be trained on the relevant digital platforms and on the project-specific editorial guidelines regarding the transcription and metadata of the letters. You will gain experience of the conventions and materiality of eighteenth-century correspondence and of scholarly activities like indexing, cross-referencing and proof-reading. The internship will develop your research skills and give you an insight into the collaborative work taking place between universities, archives and a heritage-based research centre. At the end of the project, you will have the opportunity to write a blog post detailing your observations or the knowledge you have gained from working with the letters.

Entry requirements

You should have, or be studying a degree in French or a related humanities subject, such as History or English, and have strong French language skills. An interest in the eighteenth century, correspondence and/or palaeography would be an advantage. You will be self-motivated with good attention to detail.

Chemistry 01
Interrogating immune signalling megastructures for new knowledge advances & drug discovery

Primary supervisor

Professor Yimon Aye

Project description

The goal of this project is to create chemical biology approaches to localize specific proteins to defined biological megastructures. We have reliable methods to create specific biological megastructures on demand through triggering 'adaptive immune cells', namely, 'T/B-cell receptor signalling'. These megastructures are hubs of negative regulation of this crucial proinflammatory pathway. We have also shown that these megastructures tolerate fusion of small self-labelling proteins to their component proteins. By designing and synthesizing appropriate bifunctional ligands, we will force proteins typically excluded from these crucial signalling complexes, to associate with them.

Once this association is validated by imaging, we will assay effects on signalling by antibody-based quantitative methods such as ELISA for specific cytokines. This will form the bedrock of a 'proteome-wide' screen for regulators of specific signalling processes. This project will teach students advanced molecular cloning, bioengineering, and chemical biology. We will also touch on immunology and drug design. All basics are in place in house.

Project outcome

You will have the opportunity to learn modern technologies and concepts underpinning biotechnology, bioengineering, gene cloning, chemical biology, and drug design. You will also have the opportunity to present your results in a group meeting, and as time/interest permits, to gain additional pedagogical experience through contributing to a short perspective/review article for publication as co-author.

Entry requirements

You should have, or be studying, a degree in Chemistry, Biochemistry, Molecular Biology, or a genetics-related degree.

Chemistry 02
New light emitting organic molecules with interesting spin properties

Primary supervisor

Dr Daniel Congrave

Project description

Organic molecules and materials that absorb and emit light broadly overarch traditional scientific disciplines with implications in applications related to energy, medicine, electronics and sensing. This project will expose you to the world-class synthesis and Electron Spin Resonance (ESR) facilities at Oxford Chemistry.

We have recently been pioneering organic molecules that can emit light while also featuring high-spin states. This is extremely promising for developing a new generation of molecules that can function as quantum sensors, with the ability to read-out information completely non-invasively through fluorescence.

You we be involved in developing our new fluorescent high-spin molecules, having the opportunity to see how physical and organic chemistry can link together and to learn a wide range of skills associated with this interdisciplinary research.

Project outcome

This project is interdisciplinary and combines organic and physical chemistry. It is therefore an ideal opportunity to learn a wide range of skills and be broadly exposed to scientific research so that you can make an informed choice in what to pursue in your research career after the internship. You will be involved in a collaboration between organic and physical chemists, which will provide an opportunity to improve your scientific communication skills.

You will have the opportunity to take a never before synthesized molecule from the conceptual design to the final characterization experiments after synthesizing it. You will firstly be made familiar with the computational chemistry used to design new target molecules. You will next be trained in organic synthesis, particularly air- and water-free experimental techniques, and be exposed to multiple methods of chemical purification such as chromatography and distillation. Once the new molecule is synthesized you will have the opportunity to take part in characterization experiments such as NMR, absorption and fluorescence spectroscopy, and ESR, and then be taught how to interpret the data.

You will be directly supervised in the synthetic organic chemistry lab by a PI who has a decade of experience in this type of research, and will be able to observe and be exposed to the wide range of other organic optoelectronics projects being carried out within the group. At the end of the project you will have the opportunity to write a report and may present your results to collaborative team. If any aspect of your work is included in a future publication, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a degree related to Synthetic Chemistry (eg Chemistry or Natural Sciences). Some familiarity with synthetic chemistry lab work typical of that encountered in an undergraduate degree is desirable.

Chemistry 03
Decoding the light of fungi: Exploring the role of hispidin 3-hydroxylase in bioluminescence

Primary supervisor

Dr Patrick Rabe

Project description

Hispidin 3-hydroxylase (H3H) is a key enzyme in the fungal bioluminescence pathway of Neonothopanus nambi, catalysing the final step in luciferin biosynthesis required for light emission using molecular oxygen. This self-sustaining pathway enables continuous bioluminescence, making it an attractive tool for biomedical imaging and the development of autonomous bioluminescent plants.

The project will focus on the biochemical and structural characterisation of H3H and selected mutants to define the enzyme’s active site and elucidate its reaction mechanism, identifying structural features or mutations that could optimise the wavelength and intensity of light emission. This will be achieved using advanced chemical biology approaches, biochemical assays, and recombinant protein expression, alongside crystallographic and biophysical methods to provide a detailed structural and functional understanding.

Ultimately, this knowledge will support the bioengineering of the bioluminescent pathway into plants for sustainable, tuneable carbon-neutral lighting and into other cellular systems for drug discovery and imaging.

Project outcome

This project will provide hands-on training in a variety of classical molecular biology and biochemical techniques essential for research in chemical biology, including PCR, mutagenesis, SDS-PAGE, and Western blotting, as well as protein expression and purification using yeast and bacterial systems. There will also be opportunities for training in structural and biophysical methods such as X-ray crystallography, mass spectrometry, and NMR spectroscopy.

As part of a highly interdisciplinary chemistry/chemical biology group, you will be exposed to a wide range of experimental approaches and have the flexibility to engage in additional wet-lab experiments depending on your personal interests. You will also gain experience in data analysis and scientific communication through participation in regular lab meetings. This comprehensive training will prepare you to contribute to the broader goal of bioengineering bioluminescent pathways for sustainable, carbon-neutral lighting and for applications in drug discovery and cellular imaging.

Entry requirements

You should have, or be studying, a degree in Biochemistry or a related discipline, such as Biology or Biophysics. You should also have some wet-lab experience and an interest in structural and mechanistic biology.

Chemistry 04
Inorganic materials for advanced manufacturing

Primary supervisor

Professor Simon Clarke

Project description

Students will learn skills associated with the synthesis of inorganic compounds and gain critical experience handling these. The project will focus on the synthesis of novel compounds with potential applications in fields such as catalysis, energy storage and chemical synthesis. You will be assigned a specific project in one of these areas prior to starting in the laboratory.

Project outcome

You will learn how to safely handle reactive compounds including materials that are air- and moisture-sensitive. You will analyse inorganic compounds and materials using state-of-the-art techniques (including, for example, nuclear magnetic - resonance (NMR) spectroscopy, X-ray diffraction, mass-spectrometry, electrochemical methods, electron microscopy, etc). You will also learn to interpret experimental data, write scientific reports and have the opportunity to present your research to an academic audience.

Entry requirements

You should have, or be studying, a degree in Chemistry, Materials, or a related discipline. Some familiarity with synthetic chemistry lab work typical of that encountered in an undergraduate degree is desirable.

Chemistry 05
CSHP CDT: Synthesis of resistance combatting antibiotics

Primary supervisor

Professor Chris Schofield

Project description

The design and synthesis of antibiotics targeting the mycobacterial cell wall or nucleotide biosynthesis with aim of helping developing new treatments for drug resistant tuberculosis. Importantly, since tuberculosis treating drugs will principally be used in lower middle-income countries, they must be produced in a cost-effective manner and like all future widely used antibiotics via sustainable synthesis and have minimal environmental impact. This project is linked with the EPSRC CDT in Chemical Synthesis for a Healthy Planet.

Project outcome

This project will provide hands-on training in a variety of techniques, including synthesis of small molecule antibiotics, analysis of small molecule structures using NMR and MS, introduction to computer based antibiotic design, assays for antibiotic activity using purified enzymes and microbiological studies.

Entry requirements

You should have, or be studying, a Chemistry-related degree and have standard knowledge of chemical laboratory practices at an undergraduate level. An understanding of the background to the proposed research area would be advantageous.

Chemistry 06
Three-dimensionally conjugated redox-active organic molecules

Primary supervisor

Dr Alyssa-Jennifer Avestro

Project description

Organic molecules with very well-defined spatial arrangement of heteroatoms (N, O, S) and conjugated bonding patterns of alternating single and double/triple bonds are attractive systems for studying charge transport and energy stabilisation -- important fundamental electronic properties for such small molecules to be developed as functional materials for future organic electronic and energy technologies. Typically, most organic molecules studied for this purpose feature 1D chains of conjugated bonds and/or 2D planar conjugated surfaces to transport electrons through their structures and distribute the energy evenly to achieve desirable material stabilities and conductivities (eg in organic electronic devices).

The Avestro Group is interested in escaping this popular 'flatland' paradigm by instead exploring the potential of 3D conjugated molecular shapes such as molecular hoops, spirals, tubes and frameworks to promote good electron sharing and energy storage across precisely positioned electron-storing sites in the structure. What are the advantages and added benefits of introducing this additional structural dimension to organic energy storage materials? This project aims to find out!

Project outcome

You will have the opportunity to synthesise new organic molecules that are capable of accepting and releasing charge on demand, and sharing electrons over 'long' distances on the molecular scale, which make them attractive systems for studying fundamental electronic properties that could reveal new design principles for functional materials. You will next be trained in air- and water-free organic synthetic techniques, including the use of microwave reactors to facilitate reaction times and encourage environmentally friendly conditions. You will also gain hands-on experience in cutting edge chemical purification such as automated chromatography, which will give you exposure to methods employed by chemical industry. Once new molecules are synthesized you will have the opportunity to take part in characterization experiments such as NMR spectroscopy, absorption/fluorescence and electrochemistry of their charge-released and charge-storing states -- and of course, be taught how to interpret the data! -- to understand how the charge carriers (electrons) behave within the molecular structure and unravel fundamental structure-property insights.

You will be mentored directly by the supervisor and an experienced DPhil student, participate in weekly informal research meetings to discuss current activities and goal planning for the week, and gain writing technical writing experience through the delivery of monthly reports. Summer is a typical time for the Avestro Group to also have their summer team building and ideas retreat, which we would hope you can join and bond even more with the team. At the end of the project, you will have the opportunity to write a report and may present your results to collaborative team. If any aspect of your work is included in a future publication, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a Master's (ie four-year) degree in Chemistry or a related chemical sciences subject. An interest and demonstration of strong organic synthetic skills is advantageous, as is an interest in analytical techniques applied to study the structural dynamics, electronic and optical properties of organic molecules (eg NMR spectroscopy, absorption and/or emission spectroscopy, electrochemistry). You will be enthusiastic and motivated with the ability to work independently where required.

Chemistry 07
Synthesis of aryl-fused bicycloheptanes (aryl BCHeps) as naphthelene bioisosteres.

Primary supervisor

Professor Angela Russell

Project description

While naphthalene and equivalent heterobiaryl rings are often encountered in drugs, they can be susceptible to metabolism, exhibit flat, sp2-rich structures and are almost invariably derived from fossil fuel sources, limiting their current and future utility. Yet, few viable alternatives have been described.

In this project we propose to build on our preliminary findings that aryl-fused bicyclo[3.1.1]heptanes (aryl BCHeps) can serve as effective bioisosteric replacements for naphthalene or (iso) quinolines, to optimise the synthetic route to improve efficiency and scope. The project will blend organic synthesis with computational chemistry, with an opportunity for some biochemistry. This project is linked with the EPSRC CDT in Chemical Synthesis for a Healthy Planet.

Project outcome

You will gain skills in methods and analytical techniques in organic synthesis and modern photocatalysis (Angela Russell lab), and also gain insights into and experience of computational analysis to improve reaction design (Fernanda Duarte lab). The project can encompass the design and biological testing of benzoBCHep as arylhydrocarbon receptor antagonists, giving also practical experience in biochemistry.

Entry requirements

You should have, or be studying, a Chemistry-related degree and have standard knowledge of chemical laboratory practices at an undergraduate level. An understanding of the background to the proposed research area would be advantageous.

Chemistry 08
Improving the sustainability of small ring bioisostere synthesis

Primary supervisor

Professor Edward Anderson

Project description

Bridged small ring scaffolds are becoming increasingly important in medicinal chemistry research as novel building blocks in drug design that exhibit superior properties compared to traditional motifs such as benzene rings. However, methods to access these important molecules have typically relied on pyrophoric / hazardous reagents such as organolithiums.

In this project we address this challenge by developing novel approaches to cage frameworks that do not rely on such conditions. The research will involve an array of organic synthetic chemistry methods and analytical techniques. This project is linked with the EPSRC CDT in Chemical Synthesis for a Healthy Planet.

Project outcome

You will receive training in organic synthesis and in NMR analysis of small ring frameworks, including stereochemical and regiochemical assignment. You will be required to research the project via literature searches and online databases, and will present your findings in informal fortnightly group meetings. You will summarise your findings in a report, providing training in scientific writing.
Entry requirements

You should have, or be studying, a Chemistry-related degree and have standard knowledge of chemical laboratory practices at an undergraduate level. An understanding of the background to the proposed research area would be advantageous.

Computer Science 01
Understanding and mitigating bias in medical AI

Primary supervisor

Dr Seth Flaxman

Project description

As AI for healthcare continues to advance at a rapid pace, it will become increasingly prevalent in clinical decision-making processes. However, there is a very real concern that the introduction of AI to clinics may exacerbate health outcome disparities across subgroups because algorithms can, and often do, encode protected characteristics such as age, sex, and race. The aim of this project is to gain a better understanding of how biases manifest in AI models and develop methods to mitigate them so that models can be deployed safely in clinical settings.

We will apply and develop new methods from mechanistic interpretability to inspect models and uncover their biases. We aim to discover how existing bias mitigation strategies affect the internal workings of models and design new strategies that yield fairer models.

Project outcome

Throughout the internship, you will receive guidance on the best practices for machine learning research. You will learn how to work with state-of-the-art medical foundation models and gain hands-on experience implementing their own models and experiments with deep learning libraries such as PyTorch. There will also be opportunities for you to engage in lab group meetings and present your findings to other members. Should this project result in a publication, you will be included as a co-author.

Entry requirements

You should have, or be studying, a degree in Computer Science, Mathematics, Statistics or a related discipline. Prior experience working on machine learning projects and proficiency in Python will be highly beneficial to completing this project.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Earth Sciences 01
Analysing Earth’s ancient magnetic field using some of Britain’s oldest rocks.

Primary supervisor

Dr Claire Nichols

Project description

You will analyse the palaeomagnetic signals recorded in some of Britain’s oldest rocks (metamorphic rocks, the Corodale Gneiss, from the Lewisian Gneiss Complex in South Uist) to help understand what Earth’s magnetic field was like around 1.5 billion years ago. These rocks have not previously had their magnetic properties studied; the student will generate a new dataset that will compliment published data on the Outer Hebrides. The work will be a valuable contribution to existing palaeomagnetic datasets as there is limited information about the strength of Earth’s magnetic field around 1.5 billion years ago.

You will conduct palaeomagnetic experiments (eg demagnetisation and intensity experiments) combining these with petrography (eg optical microscopy) with the potential to also conduct quantitative mineral composition analyses (eg using SEM/EPMA) to determine the magnetic and metamorphic history of rock samples of Corodale Gneiss from South Uist, Outer Hebrides, Scotland.

Project outcome

You will gain experience in working in a laboratory setting, particularly the skills associated with conducting palaeomagnetic experiments. You will develop skills in microscopy applied to metamorphic rocks and mineralogy. You will write a short report documenting your findings, with assistance from the PI’s. The work that you will do will contribute to ongoing work the supervisory team is conducting on the tectonic and magnetic history of northwest Scotland and the Outer Hebrides, that may have broader implications for our understanding of the evolution of Earth’s magnetic field in the deep past and Archean and Proterozoic tectonics. You will be listed as an author on publications from the supervisory team that your work has contributed to. You will have the opportunity to meet with other members of the PI’s research groups and practise science communication by virtue of informal presentation of your work to peers.

Entry requirements

You should be have, or be studying, a STEM-related degree. A passion for geology/earth and planetary science, or magnetism would be beneficial.

Earth Sciences 02
Tracking ocean waves as they hit the seafloor: a seismology-based analysis

Primary supervisor

Professor Jessica Hawthorne

Project description

In this project, you will explore ocean waves from the bottom up: using seismic energy that the waves transmit to the seafloor. You will examine two features of ocean waves that are hard to track visually (especially at night). You will determine how far from shore ocean waves break, and whether that distance changes with sea height. You will also determine whether particular seafloor locations within a harbour receive particularly high pressures from waves.

To explore these features, you will use some newly developed signal processing techniques, built on some approaches in speech processing. And depending on interest, you may participate in seismic deployments to make new observations.

Project outcome

You will be trained in seismology and taught to use new techniques in signal processing. You will develop skills in exploring new signals as well as in computation, particularly machine-learning based optimization. By the end of the project, you will hopefully have made new discoveries about nearshore ocean waves, which can contribute to our understanding as well as to managing ocean shoreline building. You will be guided toward publishing your results in a journal for others to access.

Entry requirements

You should have, or be studying, a Science, Computer Science, or Engineering degree with a background in mathematics. Familiarity with frequency-domain analysis, seismology, and/or Python would be useful but are not required.

Engineering 01
Predict long-term brain health from medical images when we are young

Primary supervisor

Professor Johannes Weickenmeier

Project description

As we age, the brain continuously changes its shape due to neurodegeneration and tissue damage. Medical imaging such as magnetic resonance imaging (MRI) allows us to visualize the brain at great resolution and to assess its overall state of health. We will use a large dataset of MRI from young adults to create brain models that predict their brain shape when they are older. We will then compare their aged brains against MRI of older adults and assess their risk for developing prevalent age-related neurological abnormalities. Ideally, we will be able to identify new biomarkers of accelerated brain aging with the goal to delay the onset of neurodegeneration.

Project outcome

You will be trained in computational mechanics, medical image analysis, and data management. We will use medical images to create finite element models, then run our existing aging model, and lastly develop code to analyse your simulations and to compare your results against existing databases.

Entry requirements

You should have, or be studying, a degree in a relevant discipline, such as Biology, Computer Science, Mathematics, or Mechanical Engineering. Familiarity with Python and a basic understanding of mechanics would be advantageous.

Engineering 02
Efficient Communication with LLM Agents

Primary supervisor

Dr Jialin Yu

Project description

This project investigates how large language model (LLM) agents can communicate efficiently with both humans and other agents. As multi-agent systems become more common, where AI agents collaborate, negotiate, or share information, communication efficiency becomes critical for performance and safety.

The project aims to design and analyse methods that reduce ambiguity and resource usage in these interactions, such as adaptive prompting, message compression, and causal analysis of communication failures. You will gain hands-on experience with modern LLMs (eg GPT, LLaMA) and simulation environments where multiple agents interact.

In collaboration with Microsoft, the project bridges natural language processing, causal reasoning, and distributed AI to develop agents capable of clear, efficient, and robust communication, essential for next-generation AI systems that reason and act together.

Project outcome

You will gain hands-on experience working with large language models (LLMs) such as GPT and LLaMA, learning how to design and evaluate communication strategies between humans and AI agents, as well as between multiple AI agents collaborating on shared tasks. You will be trained in prompt engineering, conversational data analysis, and basic causal reasoning techniques to diagnose communication failures and improve efficiency. The project will also provide exposure to tools for running and analysing agent interactions. You will work closely with researchers from Oxford and Microsoft, gaining insight into how research ideas are translated into real-world applications.

Entry requirements

You should have, or be studying, a Computer Science related degree and have standard software development skills at an undergraduate level. Familiarity with Python and machine learning libraries would be useful. Understanding and had previous projects in LLM and agents is preferred.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Engineering 03
Improvement and commissioning of a plasma torch facility for high-temperature material testing

Primary supervisor

Professor Tobias Hermann

Project description

The Oxford Thermofluids Institute operates several unique hypersonic and plasma-heating facilities. This project focuses on improving the small-scale plasma torch 'OPG2', a compact arc-jet used to generate high-temperature gas flows for material testing. The aim is to streamline the system for more efficient operation and better diagnostic access. The work will involve mechanical and thermal design, fabrication of new components, assembly, and testing. If progress allows, new optical temperature-measurement methods will be implemented, including DSLR imaging, emission spectroscopy, and two-colour pyrometry. The intern will gain experience in experimental design, optical diagnostics, and hands-on laboratory work, contributing directly to the creation of a refined and reliable plasma-testing platform for future research on high-temperature materials.

Project outcome

You will gain a practical understanding of how high-temperature gas flows are generated and measured, and how such conditions can be used to test aerospace materials. You will learn the fundamentals of plasma heating, thermal management, and optical diagnostics, and will be trained in mechanical design, experimental setup, and data analysis. The project will involve collaboration with engineers and researchers developing related hypersonic facilities. By the end of the internship, you will have contributed to the design and commissioning of a working scientific instrument. If results are included in subsequent studies, you may be acknowledged or named as a co-author on resulting conference or journal papers.

Entry requirements

You should have, or be studying, a degree in Engineering, Physics, Material Science, or a related discipline. No specific prior experience is required beyond enthusiasm and an interest in experimental science. A curiosity for practical problem-solving and a willingness to engage with hands-on laboratory work are essential. Skills in CAD design, electronics, or data analysis (eg MATLAB or Python) are useful but can be learned during the project.

Engineering 04
Real-time electrophysiology of monitoring in vitro tissue culture

Primary supervisor

Professor Malavika Nair

Project description

Electrical stimulation of in vitro cell cultures is a powerful technique for influencing and studying specific cellular behaviours, such as differentiation, migration, and proliferation. When an electrical potential is applied across the culture, the resulting current provides valuable information about the cells' electrical properties and their environment. The current profile depends on factors such as cell number, membrane integrity, and intercellular connectivity, allowing inference of cell viability and growth dynamics.

This project aims to analyse the electrical response of cultured cells under controlled voltage stimulation, correlating current and capacitance measurements with cell behaviour, number, and health. The study employs techniques such as impedance spectroscopy and current–voltage analysis to quantitatively assess cell-level electrophysiological properties.

Project outcome

You will gain a solid understanding of cell electrophysiology and its application to electrically stimulated tissue cultures. You will develop practical skills in performing and analysing voltage–current measurements to investigate the electrical behaviour of cells and tissues. Through this project, you will learn to interpret current and capacitance data to assess cell number, viability, and membrane properties. There will also be opportunities to contribute to the design and refinement of electronic systems for precise voltage delivery and current acquisition. By the end of the placement, you will have experience integrating biological experimentation with quantitative electrical analysis and potentially basic instrumentation development.

Entry requirements

You should have, or be studying, a degree in a STEM-related discipline. A fundamental understanding of Python will be necessary, along with some experience analysing large datasets

Engineering 05
Characterisation of shear-thickening composite hydrogels for biomedical applications

Primary supervisor

Professor Malavika Nair

Project description

Shear-thickening hydrogels are soft materials that become more resistant to flow when stressed, offering potential in areas such as impact protection, soft robotics, and biomedical engineering. This project will investigate how polymer-nanoparticle composite hydrogels respond to deformation and how their composition influences mechanical performance.

You will prepare and characterise several known shear-thickening systems using rheology, microscopy, and mechanical testing to analyse their flow and recovery behaviour. The aim is to build a detailed understanding of how nanoscale structure governs macroscopic response in complex fluids, providing a foundation for designing advanced, adaptive materials for future biomedical applications.

Project outcome

You will gain practical experience in polymer and soft-matter characterisation, including hydrogel preparation, rheological testing, and microscopy. You will develop skills in data analysis and interpretation, learning how nanoscale interactions influence macroscopic mechanical behaviour. The work will contribute to ongoing efforts to develop advanced materials for biomedical challenges, providing valuable experience in experimental research within a materials science context.

Entry requirements

You should have, or be studying, a degree in Material Science, Chemical Engineering, Chemistry, Biomedical Engineering, or a related discipline. Prior laboratory experience is desirable but not essential. No prior experience with rheology is required as full training will be provided. The ideal student will be curious, methodical, and motivated to learn experimental materials-characterisation techniques.

Engineering 06
Making plant fertilisers from air and water

Primary supervisor

Professor James Kwan

Project description

The Haber process combines nitrogen and hydrogen to make ammonia. This ammonia is converted to fertilisers, enabling mass food production. However, it is extraordinarily costly to the environment. Finding methods to generate ammonia from air and water to create truly green ammonia is the holy grail of catalysis. Sonochemistry is the application of ultrasound to nucleate bubbles that implode to create locally extreme conditions capable of producing nitrogeneous compounds (N-compounds) found in fertiliser.

This project aims to use our novel sonochemical reactor to explore the possibility of sonochemical nitrogen fixation. You will test various acoustic parameters and solvent conditions and measure the yield of nitrites and nitrates. Tests will be performed with or without catalytic cavitation agents. Emphasis on the experiments will be placed on determining the key physical (ie acoustic) and process conditions that will produce N-compounds. N-compounds will be measured using different chemical assays.

Project outcome

You will produce green N-compounds from water and air and quantify it using chemical assays and liquid chromatography. You will be trained in general operation of ultrasound devices and associated electronics.

Entry requirements

You should have, or be studying, a degree in Engineering, Materials, Chemistry or Physics. You should also possess familiarity with MatLab.

Engineering 07
Understanding hydrogen-metal interactions to enable a green energy transition

Primary supervisor

Professor Emilio Martínez-Pañeda

Project description

Hydrogen is said to be both a blessing and a curse. It is ubiquitous, and its applications will drive the technology of a net-zero carbon society. However, it is also infamous for 'embrittling' metallic materials, reducing by orders of magnitude their ductility (elongation), fracture toughness and fatigue crack growth resistance. This so-called hydrogen embrittlement phenomenon is responsible for numerous hydrogen-assisted failures across the transport, defence, construction and energy sectors and, importantly, is considered one of the biggest impediments to the broader implementation of a hydrogen-based fuel economy, hindering the transition away from fossil fuels.

This project will use experimental techniques (electrochemical, mechanical) to understand how hydrogen degrades metals and develop new materials that can enable a green hydrogen energy infrastructure.

Project outcome

Research will be conducted that can lead to scientific publications. The work will involve the use of electrochemical (permeation, desorption, galvanostatic/potentiostatic charging) and/or mechanical (tensile tests, fracture, fatigue) techniques, along with material characterisation to provide new understanding of the interaction of hydrogen with: (i) 3D printed metals, an important area that remains largely unexplored, and (ii) new materials that hold promise in being suitable for hydrogen transport and storage.

Entry requirements

You should have, or be studying, Engineering, Materials, Physics, or a related discipline.

Engineering 08
Benchmarking foundation models on large and longitudinal intensive care data

Primary supervisor

Professor Tingting Zhu

Project description

Foundation models are transforming healthcare AI, but their evaluation in high-stakes clinical settings remains inadequate. This project will be developing standardised evaluation protocols for foundation models in intensive care units (ICUs). You will integrate multiple large-scale ICU datasets (MIMIC-IV, eICU, and others) to create a comprehensive benchmark for evaluating foundation models on realistic clinical tasks. This involves harmonising multimodal data from different sources, handling the temporal alignment of asynchronous streams (such as vital signs, laboratory results, and clinical notes), and preprocessing longitudinal patient trajectories. You will then fine-tune foundation models and establish baseline performance on critical ICU tasks, including risk stratification, clinical deterioration prediction, and resource allocation. Your contributions will inform the development of reproducible evaluation standards. The benchmark dataset and code will be released openly to the research community.

Project outcome

You will:

1. gain a solid background in multimodal healthcare data integration, including handling electronic health records, time-series vital signs, and clinical notes;
2. become familiar with foundation models (large language models and multimodal models) and fine-tuning approaches for healthcare applications;
3. develop expertise in benchmark design principles, including dataset construction, evaluation metrics, and reproducibility standards;
4. implement data preprocessing pipelines and baseline models using modern deep learning frameworks; and
5. contribute to open-source tools and documentation that will be used by the wider research community.

Entry requirements

You should have, or be studying, a degree in Engineering, Mathematics, Statistics, Computer Science, or a related quantitative discipline. Familiarity with Python is strongly required, with experience in PyTorch, TensorFlow, or similar deep learning frameworks highly desirable. Prior coursework or projects involving machine learning, statistical modelling, or data analysis would be advantageous.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Engineering 09
Cracking the ice: Coupling glacier flow with crevasse formation at continental scale

Primary supervisor

Dr Tim Hageman

Project description

Glaciers don’t just slowly flow downhill and melt, they can break. Predicting when and where crevasses form is key for sea-level rise forecasts and glacier-related hazards. This project builds a next-generation model that couples glacier flow with fracture mechanics, scaling from valley glaciers and small ice-shelves to whole ice sheets. The aim of this specific part of the research is a working coupled scheme that captures both smooth viscous flow and brittle failure.

This project offers several avenues for research projects, depending on your interest/background, including:

1. computational modelling, extending an existing ice-flow code to capture damage/fractures and experimenting with the numerical schemes used for this; and
2. geo-data processing, assembling continent-scale datasets and modifying existing models to interface with this data, allowing the existing schemes for small benchmark cases to be applied to real glaciers.

You’ll learn how to integrate fracture physics into large models, test the sensitivity of crevasse development to stress, temperature and geometry, and package results such that they are replicable and re-usable by others.

Project outcome

You will gain hands-on experience in numerical glacier modelling and fracture mechanics, practical coding skills, version control with Git, and reproducible research workflows. You will build and test a prototype coupled flow-fracture module, or develop a continent-scale geo-data pipeline. You will also have the ability to participate in research group meetings, and present the outcomes of your research during them.

Entry requirements

You should have, or be studying, a relevant degree, such as Engineering, Earth Science, Environmental Science, Physics, Mathematics, or Computer Science.

You should have an interest in numerical methods and/or geospatial data and you should be comfortable coding in Python or have a willingness to learn new tools. Experience with numerical modelling, version control (git), and working with geodata would be advantageous.

Engineering 10
Reasoning enhancement in large language models for healthcare applications

Primary supervisor

Professor Tingting Zhu

Project description

Recent advances in large language models (LLMs) have enabled remarkable performance in text understanding, reasoning, and generation. However, their potential for healthcare applications remains limited by challenges such as unreliable reasoning, lack of domain adaptation, and interpretability. This project aims to explore how reasoning enhancement techniques, such as chain-of-thought (CoT) prompting, retrieval-augmented generation (RAG), and fine-tuning with medical datasets, can improve the robustness and transparency of model outputs in healthcare contexts.

You will experiment with open-source models (eg Llama, Mistral) and medical benchmark datasets to evaluate how these methods affect factual accuracy and reasoning quality. The ultimate goal is to contribute towards developing safer, more interpretable AI systems that can support clinicians and medical researchers in decision-making.

Project outcome

You will:

1. gain a strong background in natural language processing and large language model architectures;
2. learn how to perform data pre-processing, prompt design, and fine-tuning using medical or biomedical datasets;
3. develop experience in experimental design, model evaluation, and reasoning analysis;
4. acquire skills in using tools such as Python, PyTorch, and Hugging Face libraries; and
5. write a final report comparing your results against baseline methods, with the potential to contribute to a research publication or workshop paper.

Entry requirements

You should have, or be studying, a degree in Computer Science, Data Science, Engineering, Mathematics, or a related quantitative discipline. Familiarity with Python is strongly encouraged, and experience with machine learning or deep learning frameworks (eg PyTorch or TensorFlow) would be advantageous. An interest in artificial intelligence, reasoning, or healthcare applications is desirable.
Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Engineering 11
Digital equity in disaster scenarios

Primary supervisor

Professor Noa Zilberman

Project description

The world is threatened by disasters ranging from severe weather events to pandemics. While technologies like remote healthcare and online education offer critical support, not everyone has equal access to them. This digital divide creates significant inequality during crises, as many cannot use these tools even when available.

As part of this project, you will develop hardware or software mechanisms to improve digital equity for UK residents during disasters. The goal is to enhance accessibility to key digital services. Solutions might include autonomous remote healthcare monitoring, alerting systems, or low-cost, low-latency AI services for the home.

This project is interdisciplinary and covers topics in computer architecture, computer networks, software engineering, and digital equity. AI is an optional, relevant field of study for this work.

Project outcome

As part of this project you will choose to focus on hardware, software or hardware/software co-design. You will explore advanced computing technologies, efficient coding practices, digital equity and potentially AI. You will gain experience in advanced data processing, scripting, power/performance-aware programming, software/hardware development, publishing code and artefacts, and you will help improve digital equity in the UK and our preparedness to future crises. If any publication opportunities will arise from the project, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a degree in Computer Engineering, Electrical Engineering, or Computer Science. Excellent programming skills in Python or C/C++ or Verilog and basic knowledge in computer architecture or computer networks are required.

Engineering 12
Artificial intelligence for cancer detection in medical imaging

Primary supervisor

Professor Jens Rittscher

Project description

This project will investigate how artificial intelligence can be used to support cancer detection in medical images, for example endoscopic images and videos and other publicly available imaging datasets. The focus will be on designing and evaluating deep learning methods for tasks such as lesion segmentation and detection in realistic data.

You will work with de-identified and public datasets, curate suitable training and evaluation subsets, and implement model training and evaluation pipelines in Python and PyTorch. Supervision will be provided through regular meetings, discussion of experimental plans, and feedback on results, but the work will not follow a step-by-step tutorial format. Instead, you will be encouraged to take intellectual ownership of a well-defined research question and to explore model design choices and performance trade-offs in a systematic manner.

Project outcome

You will gain experience working with medical imaging data (for example, endoscopy images and videos) and additional public datasets, including basic pre-processing and organisation. You will have the opportunity to develop practical skills in data curation, including constructing, cleaning, and documenting training, validation, and test sets. You will implement and train deep learning models in Python and PyTorch for segmentation and/or detection, and evaluate them using appropriate quantitative metrics. You will learn how to design and run controlled experiments (eg comparing architectures, hyperparameters, or data splits) and interpret the results in a clinically relevant context and you will develop scientific communication skills through written documentation and an end-of-project presentation to the research group.

Entry requirements

You should have prior experience in Python and implementing and training deep learning models using PyTorch. You should also have familiarity with basic machine learning concepts, such as, training, validation, test splits, loss functions, optimisation, overfitting, and/or evaluation metrics). Experience with image data and computer vision would be advantageous. No prior medical or biological training is required.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Internet Institute 02
Public interest technology research on AI social biases

Primary supervisor

Professor Luc Rocher

Project description

AI systems often appear neutral but still reproduce prejudice and biases tied to gender, race, age, disability, or socioeconomic status. These effects can emerge in language models, predictive classifiers, recommender systems, privacy techniques designed to safeguard data, or surveillance frameworks used to monitor behaviour. They can lead to real and significant harms by shaping how people are represented and treated.

This project broadly investigates how such technologies encode and amplify social biases and how these dynamics feed back into real peoples’ experiences. The scope is deliberately open: you can examine representational patterns in LLM outputs, measure performance drops in classification systems, study how privacy technologies alter the visibility of minority groups in data, or explore the impact of mass surveillance technologies.

Depending on your interests, you might design a new evaluation method, help us conduct a study with human participants to measure real-world effects, or analyse large existing datasets to identify systematic disparities. You could also work on building an interactive web tool to convey the risks/benefits of using Machine Learning systems to the general public.

Project outcome

You will gain experience analysing the social impact of ML-based systems. You will learn to construct targeted evaluations, interpret model and system behaviour, and/or design empirical studies involving human participants. You will strengthen your programming and data analysis skills.

Entry requirements

You should have, or be studying, a degree in Computer Science, Artificial Intelligence, or a related STEM discipline. A solid understanding of machine learning concepts is essential, along with proficiency in programming languages such as Python. Experience with data analysis and visualisation tools will also be advantageous. Successful candidates will be working on real research projects and so must be self-motivated and fit into a team of researchers from various backgrounds. Good communication skills are important, as you will be expected to present findings clearly to the research team.

Materials 01
High-energy-density Na-rich disordered rocksalt cathodes

Primary supervisor

Professor Peter Bruce

Project description

Na-ion batteries could offer more sustainable alternatives to Li-ion batteries as they are much cheaper and use more earth-abundant minerals, but they fall short on performance, particularly energy density. One promising route to boost the amount of sodium and electrons that cathode materials can store is 'sodium-rich' cathodes: materials with more sodium than transition-metal (TM) atoms. Examples of sodium-rich cathodes being investigated include Na2TiS3, which exhibits a layered structure.

This project aims to investigate a different material family: Na-rich disordered rocksalt cathodes, where sodium and TM ions are randomly distributed. Such disorder offers a remarkable composition flexibility, which can potentially support diverse redox chemistries and enhanced electrochemical performance. The project will involve the synthesis and structural characterisation of new cathode compounds to explore part of this phase space and cell testing to examine the electrochemical performance of these cathodes.

Project outcome

You will gain hands-on experience and theoretical knowledge in inorganic synthesis, crystallography, and electrochemistry. This project will provide you with a broader perspective on current developments and research trends in battery technology and energy storage, within a world-leading research environment. You will also have the opportunity to engage with researchers from diverse backgrounds, providing valuable insights into potential career paths and further study opportunities. In addition, you may have the opportunity to participate in synchrotron experiments involving advanced spectroscopy and diffraction techniques at the Diamond Light Source. At the end of the project, you will be invited to present your findings to the research group in an internal meeting. If any part of your data or analysis is included in a future publication, you will be listed as a co-author on that paper.

Entry requirements

You should have, or be studying, a degree in materials science, chemistry, or a related discipline. An interest in battery technology would be advantageous.

Materials 02
Mechanical characterisation of high entropy alloy

Primary supervisor

Professor Angus Wilkinson

Project description

High entropy alloys have multiple elements present in large molar fractions so that no element is the clear matrix or solvent. The project will look at alloys developed with sustainability in mind as they can be recycled from scrap readily available waste streams. We will continue development of novel bend testing strategies using digital image correlation to extend analysis into the plastic regime. Deformed samples will be characterized using optical and scanning electron microscope methods focusing slip features as a function of the strain level (which varies spatially in the bend test geometry). Strain conditions corresponding to the onset of primary slip, and multiple slip conditions will be identified. These will be compared to implied hardening rates from bend test data. Systematic differences across alloy chemistry will be explored.

Project outcome

You will gain experience of metallography, mechanical testing including digital image correlation, optical microscopy, and scanning electron microscopy (possibly with EBSD and EDS). You will use and develop matlab scripts for data analysis. You will be welcomed to Oxford Micromechanics Group (OMG) meetings and will be invited to present you finding to OMG towards the end your internship. You will be included as a co-author on any publications/presentations that include your data and analysis.

Entry requirements

You should have, or be studying, a degree with elements of Materials, such as Material Science, Mechanical/Aerospace/General Engineering, or Physics. Existing knowledge or experience of beam theory, metallography, optical microscopy, and/or matlab would be useful but not essential as training will be provided.

Maths 01
How does cutting social contacts affect the spread of disease? A mathematical investigation

Primary supervisor

Professor Ruth Baker

Project description

The classical SIR (Susceptible-Infectious-Recovered) model of disease spread assumes a well-mixed population where each individual is equally likely to interact with any other. In reality, social contacts are structured and limited, and during outbreaks, individuals often reduce their number of contacts through, for example, social distancing. This project investigates how contact pruning (the permanent or temporary removal of edges from a contact network) affects disease spread.

We will simulate SIR dynamics on networks with different structures and levels of edge pruning to determine how much contact reduction is necessary to mitigate an outbreak, and which strategies are most effective. The project blends theoretical analysis with simulation, and aims to provide insight into the effectiveness of real-world interventions that correspond to contact pruning.

Project outcome

Through this project, you will gain skills in network modelling and epidemic simulation using Python. You will learn to construct synthetic networks such as Erdős–Rényi, Watts–Strogatz, and scale-free graphs with the NetworkX Python library, and to implement discrete-time SIR models with fixed transmission and recovery rates.

You will also gain experience in quantitative analysis by running simulations across varying network structures and pruning intensities, measuring outbreak size and timing, and comparing outcomes to unmodified networks. In addition, you will learn basic results from percolation theory and mean-field SIR dynamics, and use them to predict critical thresholds for outbreak spread under pruning, and degree distribution and clustering metrics will be compared with observed simulation outcomes.

Entry requirements

You should have, or be studying, a degree in Mathematics, with experience in probability and differential equations. Familiarity with basic concepts in graph theory and dynamical systems is desirable but not essential. Prior experience with Python programming would also be ideal, particularly for data analysis and numerical simulation.

Physics 01
Ultracold atom laboratory

Primary supervisor

Professor Robert Smith

Project description

The project will be based in our ultracold atom laboratory in which we cool erbium and potassium atoms down to nano-Kelvin temperatures to study many-body quantum phenomena such as the recently realised supersolid state. The details of the project you will be working on will be finalised later but could involve design and construction of optical setups for trapping or imaging ultracold atoms, generation of custom magnetic fields for manipulation of atomic properties or numerical simulation of ultracold atom clouds.

Project outcome

You will gain experience in experimental techniques in areas such as optics, electronics, and data analysis, and gain insight into quantum gases. You will write up your work in a short report and if particularly successful you could be a co-author on a publication.

Entry requirements

You should have, or be studying, a degree in Physics (or equivalent).

Physics 02
Accelerating particle accelerators: using reduced-precision floating-point numbers to efficiently model ultra-relativistic beam dynamics

Primary supervisor

Professor Adrian Oeftiger

Project description

Hamiltonian dynamics has broad applicability, from spacecraft trajectories to solar plasmas. Exploiting the geometric properties of Hamiltonian systems extends the horizon for numerical errors in simulations with long time scales, critical for accurate tracking of gravitational systems or proton beams in colliders like LHC. Ultra-relativistic beams in future colliders and existing electron-based X-ray facilities introduce a challenge: synchrotron radiation acts as a random dissipative force, breaking the Hamiltonian framework. Does this make simulations easier or harder to run reliably?

This project investigates how numerical errors accumulate in ultra-relativistic beam dynamics, investigating how these are modified by nonlinear perturbations and chaotic regimes. Conventional methods demand high-precision computing, which is memory-intensive. By studying robustness to low-precision floating-point arithmetic, this work explores efficient use of parallel computation on GPUs. Running many cheap simulations could allow averaging to mitigate numerical noise, potentially offering a practical path forward for future ultra-relativistic beam tracking.

Project outcome

Participating in this project teaches you about the consequences of discretising physics models in order to perform simulations. Such numerical experiments allow for more freedom to investigate physical phenomena. Completing this project successfully will require intuiting the former and developing the latter. While this project focuses on particle accelerators, the gained experience is applicable to any research in computational physics. Beyond developing computational modelling skills, depending on progress in the project, you may have the opportunity to have your results written-up for publication in either an international conference or potentially a scientific journal.

Entry requirements

You should have, or be studying, a degree in Physics, Mathematics, or Computer Science. You must have an interest in computational modelling. A familiarity with coding is useful and knowledge of handling computations on GPUs would be advantageous.

Physics 03
Observing the atmosphere from space

Primary supervisor

Dr Anu Dudhia

Project description

The project involves working within a group specialising in using satellite measurements of the Earth's infrared emission spectrum to retrieve concentrations of a number of different atmospheric gases normally only present in small concentrations (SO2, NH3, C2H6, etc).

The project will involve writing Python code to analyse our data and compare these with other datasets, culminating in a written report and a final presentation to the group. You will be based in an office with other summer project students, with whom they are expected to collaborate, and also participate in weekly group meetings.

Project outcome

You will gain experience in Python coding to access, manipulate and display large datasets, collaborative working within a research group, giving formal presentations, writing a research report.

You will produce a report summarising an investigation into the retrieval of a particular molecule of interest (to be decided nearer the time).

Entry requirements

You should have, or be studying, a degree in Physics or a related STEM subject and have Python programming experience.

Physics 04
Observing volcanic clouds from space

Primary supervisor

Dr Anu Dudhia

Project description

In the Earth Observation Data Group, we have several tools which can be used to detect and quantify information about volcanic clouds (including sulfur dioxide, ash, sulfuric acid) using high spectral resolution measurements. This is critical for understanding the impacts of eruptions on aviation, the atmosphere and climate. We are interested in expanding our tools to incorporate newly launched satellite instruments such as the InfraRed Sounder which will make measurements over Europe every 30 minutes.

You will have the opportunity to work with some of these new instruments – helping to create tools which are used in the future for studying eruptions. Work done during this project will feed into an ongoing collaboration with the UK Met Office where we hope to run these techniques in the long term.

Project outcome

You will gain experience in Python to access, manipulate and display large datasets. You will work collaboratively within a research group and give a presentation at the end of the project. You will also produce a report summarising the results of the study.

Entry requirements

You should have, be studying, a degree in Physics, Earth Sciences or a related STEM subject, and have some Python programming experience.

Physics 05
Hunting for supersymmetric dark matter at the Large Hadron Collider

Primary supervisor

Professor Alan Barr

Project description

Dark matter is one of the most prominent puzzles in modern particle physics. The mystery may be solved by Supersymmetry, a theory that introduces a suite of new fundamental particles which physicists are searching for at the Large Hadron Collider (LHC), the world’s most powerful particle accelerator.

In this project we will explore the hints of supersymmetry that could be present in LHC data. There are several directions the project could take depending on the interests of the student, including applying machine learning methods, statistical techniques and particle physics phenomenology.

Project outcome

Depending on your interests, you will gain experience using machine learning methods, statistical techniques, and/or particle physics phenomenology. The results of the project will motivate future Large Hadron Collider analyses. At the end of the project you will present your findings to our research groups and produce a written report.

Entry requirements

You should have, or be studying, a degree in Physics. Experience with Python, or a desire to learn, is essential. Knowledge/skills in statistics and C++ may be advantageous but are not essential.

Statistics 01
Machine Learning and AI for SARS-CoV-2 Mpro Inhibitor Discovery

Primary supervisor

Professor Garrett Morris

Project description

You will learn how to apply the latest machine learning and AI technologies to help discover new inhibitors of a key drug target in SARS-CoV-2, the virus that causes COVID 19. By training models on binding data and 3D atomic structures of inhibitors of SARS CoV-2 main protease, you will advance our understanding of how to block viral maturation and how to develop new drugs to treat COVID-19.

Project outcome

You will explore data from the COVID Moonshot project to develop a variety of classical ML models and more advanced methods such as Graph Neural Networks, Atomic Environment Vector-based models, and molecular transformers.

Entry requirements

You should have, or be studying, a degree in Computer Science, Statistics, Engineering, Bioinformatics, or another relevant discipline. This project is suitable for those seeking to improve their knowledge of practical machine learning and complex deep learning architectures, and learn the application of such models to protein-small molecule complex structures. You should have experience of machine learning and programming with Python. Experience with the deep learning framework PyTorch would be beneficial.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Statistics 02
Understanding the Effects of Data Quantity and Label Noise on Machine Learning Models in Drug Discovery

Primary supervisor

Dr Fergus Imrie

Project description

This project explores how data quality and quantity affect the performance of machine learning models in drug discovery. In particular, it focuses on QSAR modelling, where computers learn to predict how strongly different molecules might interact with a biological target. Experimental data is often limited and can contain errors (known as label noise), which can reduce model reliability.

Using publicly available data, the project will systematically vary the amount of training data and level of label noise to observe the impact on the performance of machine learning models. The project could then investigate the use of data-centric methods (approaches that focus on the data and its quality, or lack thereof, rather than changing the model) to reduce the impact of noisy measurements. The ultimate aim is to develop practical guidance and techniques for building robust QSAR models when working with imperfect experimental data.

Project outcome

You will gain hands-on experience in computational data analysis, machine learning, and cheminformatics tools, and use them to build and evaluate QSAR models. This project will also develop your ability to visualise results, interpret model behaviour, and communicate scientific findings. Throughout the project, you will be supported in understanding key concepts in machine learning for drug discovery.

You will present your findings in an internal meeting at the end of the project. You may have the opportunity to contribute to a publishable research project (either standalone or as part of other projects), for which you would be included as a named co-author.

Entry requirements

You should have, or be studying, a degree in Computer Science, Statistics, Engineering, Biochemistry, Bioinformatics, or another relevant field.

This project is suitable for those seeking to improve their practical knowledge of machine learning, and learn to apply such models to real-world use cases, such as drug discovery.

You should have experience of machine learning and programming with Python. Experience with machine learning frameworks, such as scikit-learn or PyTorch, or molecular data would be beneficial, but is not required.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Statistics 03
Mathematical modelling of the drivers and dynamics of dengue epidemics

Primary supervisor

Dr Cathal Mills

Project description

Dengue is a mosquito-borne disease, whose distribution and incidence are expanding and increasing globally. With environmental sensitivities across the mosquito and virus, dengue primarily affects low- and middle-income countries in tropical and sub-tropical regions. However, climate change, urbanisation, and globalisation coalesce to impose growing public health burdens on resource-limited settings. Faced with this complex public health and environmental challenge, decision-makers need timely and reliable information to respond appropriately.

In this project, you will aim to improve our ability to understand and control dengue outbreaks. Recent mathematical models offer avenues to investigate the time- and space-varying causal mechanisms of large dengue outbreaks. We can now describe dengue virus transmission richly using partial differential equations across populations of infected and infectious mosquitoes and humans. However, it is not yet understood how best to implement such frameworks in real time, how different environmental factors interact with each other and with human and biological confounders, or how to incorporate the effects of stochasticity.

Using numerical and analytical approaches, the aim of this project will be to use mathematical modelling to contribute meaningfully to understanding and controlling dengue epidemics.

Project outcome

You will have the opportunity to develop numerical and analytical approaches for investigating systems of partial differential equations. These approaches will blend mathematical biology with modern machine learning and statistical inference techniques. You will have the opportunity to present your results to the group throughout the internship. At the end of the internship, you may have the opportunity to contribute to important ongoing or new publishable work, for which co-authorship would be recognised.

Entry requirements

You should have, or be studying, a degree in Mathematics or Statistics and be familiar with a programming language of your choice.

Statistics 04
Anytime-valid inference via coin betting

Primary supervisor

Professor Patrick Rebeschini

Project description

This project investigates anytime-valid statistical inference through the lens of the coin betting framework. Traditional statistical tests assume a fixed sample size, but modern data analysis often involves continuous monitoring and adaptive decision-making. Coin betting offers a principled approach to address this challenge by constructing supermartingales—wealth processes that remain valid under the null hypothesis—leading to e-values and confidence sequences that maintain correct coverage at all times.

You will explore how coin betting can be used to design new sequential testing and estimation procedures with strong finite-sample guarantees. Potential directions include developing e-processes for high-dimensional or dependent data, analysing trade-offs between adaptivity, power, and confidence width, and studying the duality between statistical evidence and wealth accumulation. The project emphasizes rigorous theoretical analysis, with optional simulations to demonstrate the robustness and practicality of the proposed inference methods.

Project outcome

You will gain experience in modern statistical methodology, with a focus on sequential inference and the coin betting framework. You will be trained in how to construct and analyse supermartingales and e-values, derive confidence sequences, and apply these tools to real-world settings such as A/B testing used eg for website/game design optimisation. You will also develop skills in theoretical analysis and mathematical proof, supported by computational experimentation using Python to illustrate key results.

Throughout the project, you will receive guidance on communicating complex theoretical ideas clearly and rigorously. At the end of the project, you will present your findings in an internal seminar and prepare a short, written report summarizing your contributions. If your theoretical or methodological developments contribute to future research outputs, you may be acknowledged or included as a named co-author on a resulting publication.

Entry requirements

You should have, or be studying, a degree with elements of probability theory, ideally at the level of martingales. Familiarity with Python would be advantageous.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Biochemistry 01
Bacterial communication through extracellular vesicles

Primary supervisor

Professor Lindsay Baker

Project description

Bacteria live in complex environments and communities. In order to successfully navigate this, bacteria must be able to communicate across space. One way in which this is done is via extracellular vesicles (bEVs), particles that are released from cells and possess both membrane and soluble components. In this project, we aim to understand how the proteins trafficked in these vesicles influence community behaviour and ability to persevere through antimicrobial stress. This will be accomplished through a combination of structural biology and microbiology techniques. Upon isolation of bEVs from antibiotic-treated cultures, we will analyse the morphologies of bEVs via electron cryotomography. Additionally, we will probe the biological action of these isolates on healthy bacterial cells.

Project outcome

You will gain experience in aseptic microbial culture and harvesting and purification of extracellular vesicles. You will have the opportunity to prepare samples for electron cryotomography and mass spectrometry, as well as participating in the data collection and processing for the former.

Entry requirements

You should have, or be studying, a molecular life sciences degree, such as Biochemistry or Microbiology. Familiarity with bacterial culture would be useful.

Biology 01
Synthetic chloroplasts for improved photosynthesis

Primary supervisor

Professor Francesco Licausi

Project description

This research project aims to design and generate a synthetic chloroplast genome for potato (Solanum tuberosum) to enhance photosynthetic efficiency and resilience under high light and heat stress conditions. Climate change increasingly exposes crops to environmental extremes, leading to significant reductions in yield. By integrating advanced synthetic biology, genomics, and bioinformatics approaches, this project will redesign key chloroplast genes and regulatory elements involved in photosystem stability, photoprotection, and carbon fixation. The synthetic genome will be assembled and introduced into potato chloroplasts using targeted transformation techniques. Physiological, biochemical, and transcriptomic analyses will assess improvements in photosynthetic performance, thermotolerance, and growth under stress.

This work will establish a framework for chloroplast genome engineering as a sustainable strategy to improve crop productivity and climate resilience. Ultimately, the project seeks to provide a proof of concept for synthetic organelle genomes as tools for next-generation crop improvement.

Project outcome

You will gain hands-on experience in synthetic biology, molecular cloning, and chloroplast genome engineering. You will develop skills in bioinformatics tools for genome design, sequence optimization, and comparative genomics. You will learn laboratory techniques such as DNA assembly, transformation, and plant tissue culture, as well as physiological and molecular assays to evaluate photosynthetic performance under stress. Additionally, you will gain experience in data analysis, scientific documentation, and interdisciplinary collaboration. This project will provide a strong foundation in plant biotechnology and equip you with valuable skills for careers in genetic engineering and crop improvement research.

Entry requirements

You should have, or be studying, a degree in Biology or Biochemistry. Knowledge of plant physiology would be advantageous.

Biology 02
The effect of elevated temperatures on plant disease development

Primary supervisor

Professor Gail Preston

Project description

Current climate models project a global average increase of air temperatures with more frequent temperature extremes accompanied by changes in precipitation events across the globe. Recent extreme weather events have been linked to severe local outbreaks for various plant pathogens. Plants and pathogens interact in complex ways, with the outcome of plant-pathogen interactions determined by various factors including both environmental factors and host and pathogen genetics.

In this project you will study how elevated temperatures and other abiotic factors affect virulence gene expression in economically important bacterial plant pathogens such as Pseudomonas syringae, using luminescent and fluorescent bioreporter systems that allow us to monitor virulence gene expression in vitro and in planta.

Project outcome

You will gain hands on experience of microbiology, molecular biology and imaging techniques and associated data analyses. You will be fully integrated into the research group during your internship and participate in and present in lab meetings. If any aspect of your work is included in a future publication, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying a science-related degree, such as Biology, Biochemistry, Chemistry or Physics. Some knowledge of microbiology or cellular processes could be useful.

Biology 03
Functional analysis of TOC complex assembly factors

Primary supervisor

Professor Paul Jarvis

Project description

Chloroplasts are organelles in plant cells that capture light energy for photosynthesis, and their function depends on thousands of proteins imported from the cytosol. Protein import is controlled by large multi-protein complexes in the chloroplast outer (TOC) and inner (TIC) membranes, but we don't yet fully understand how these complexes assemble to carry out this function.

The project will focus on exploring how proteins of interest, OEP80 and AKR2A, help assemble the TOC complex. This will be achieved using fluorescent tags that allow us to visualise how these proteins interact with components of the TOC complex and where in the cell these interactions occur. Experiments will include plant cell isolation, protein expression, and visualisation by fluorescence microscopy. A better understanding of how the TOC complex is assembled will be a fundamental step in developing approaches aimed at improving plant growth and development under ever increasing environmental and anthropogenic stress.

Project outcome

You will be immersed into the research project and gain experience in a wide range of topics, from managing a small research project, developing interdisciplinary skills in the lab, receiving training in core techniques such as plant cell isolation, protein expression in plants, SDS-PAGE and immunoblotting, and confocal fluorescence microscopy, analysing and troubleshooting results, applying statistical analyses to make your conclusions more meaningful, and developing materials (figures, presentations and report) and skills to communicate your results to others. If your results make a significant contribution to a future publication, you will be included as a co-author in recognition of your contribution to the field during your time with us.

Entry requirements

You should have, or be studying, a degree related to Biological Sciences, Molecular Biology, Cell Biology, or Biochemistry with a strong interest in plants. Having some lab experience and the ability to work independently will be useful, although any required training and support will be provided.

Biology 04
From Wuhan to the world: A genomic reconstruction of early SARS-CoV-2 spread

Primary supervisor

Dr Mahan Ghafari

Project description

This project will explore how SARS-CoV-2, the virus causing COVID-19, spread globally in the early stages of the pandemic using publicly available viral genome sequences. You will learn how to assess sequence quality, align genomes, and place them on a phylogenetic tree to see how samples cluster by time and place. By examining these clusters, we will infer patterns of introduction, early transmission, and how new mutations emerged and rose in frequency. The project will use computational tools common in genomic epidemiology (eg sequence QC, multiple sequence alignment, tree visualisation) and will culminate in a short report on what the data reveal about early viral evolution and spread.

Project outcome

You will learn how to work with real SARS-CoV-2 genomic data, including downloading public sequences, performing basic quality control, conducting evolutionary analysis, and exploring how sequences group on a phylogenetic tree. You will gain hands-on experience with core tools in genomic epidemiology (sequence alignment, tree visualisation, and simple mutation tracking) and learn how these analyses inform our understanding of viral evolution and global spread. You will also practice summarising genomic findings for a non-specialist audience and present your results to the group at the end of the internship.

Entry requirements

You should have, or be studying, a degree in a relevant subject such as Biology, Biomedical Sciences, Biochemistry, Genetics, or a quantitative discipline (eg Mathematics, Statistics, or Computer Science). Basic computer skills and an interest in working with data are encouraged, but prior experience in bioinformatics is not mandatory as training will be provided. A keen interest in viruses, infectious disease, or how SARS-CoV-2 evolves and spreads is essential. The project will suit a motivated student who is curious, comfortable learning new software, and able to work carefully with real-world data.

Biology 05
Seeing nature through citizen eyes: analysing flower colour and spread in an invasive species

Primary supervisor

Professor Rob Salguero-Gomez

Project description

This project will explore patterns of flower colour and distribution in an invasive plant species using photographs submitted by the public through citizen science platforms. You will collect, organise, and analyse image data to investigate how flower colour varies across regions and whether environmental factors influence these patterns. You will gain hands-on experience in data management, programming, and the use of accessible artificial intelligence tools to help automate image labelling and analysis. The project also involves spatial data exploration to understand distribution trends. Through this work, you will develop transferable coding and research skills, deepen your understanding of invasive species ecology, and appreciate the value of citizen science in advancing environmental research and engaging the public in scientific discovery.

Project outcome

You will gain experience in handling and analysing large citizen science image datasets, learning how to extract meaningful information about flower colour and distribution patterns. You will be trained in programming (using Python or R), data cleaning, image classification, and introductory machine learning methods to support automated labelling. You will also learn principles of programming reproducibility and how to create and maintain your own coding portfolio using GitHub.

By the end of the project, you will have produced a short written report and co-created a video explaining your findings. You will also present your results to the research group and may be acknowledged in future research outputs arising from the project.

Entry requirements

You should have, or be studying, a degree in Biology, Environmental Science, Geography, or a related quantitative subject such as Data Science or Computer Science. Some experience with data handling, programming (preferably in Python or R), and an interest in applying computational tools to ecological questions would be advantageous. Familiarity with image analysis, machine learning, or GIS software is desirable but not essential, as training will be provided. You should be curious, motivated to learn new analytical techniques, and comfortable working independently with guidance. An enthusiasm for biodiversity, citizen science, and communicating research to the public will help you get the most from this project.

Biology 06
How does cacao and coffee farming impact soil organic carbon?

Primary supervisor

Dr Joseph Poore

Project description

Changes in soil organic carbon (SOC) stocks on agricultural land can significantly influence CO2 emissions or removals. Adopting land management practices that increase SOC offers opportunities to reduce atmospheric CO2 and offset the carbon footprint of agricultural production. To better understand these effects, we are compiling the world’s largest harmonised archive of SOC data, linked to production practices and land-use history, enabling identification of drivers of SOC changes.

This project focuses on cacao and coffee systems globally, covering 11.7 and 12.2 million hectares, respectively, in 2023. Both crops are major drivers of deforestation in tropical regions, including Indonesia, Côte d’Ivoire, and Brazil. The internship will investigate SOC dynamics when forests are converted to plantations and assess how agroforestry can mitigate SOC losses compared with monocropping.

The main task is uploading SOC sampling data to the HESTIA platform, evaluating the effects of land management and land-use changes, with automatic calculations of associated CO2 emissions or removals and their broader life-cycle impacts.

Project outcome

You will gain knowledge of the role of soil organic carbon sequestration in climate change mitigation efforts in the agricultural sector and how greenhouse gas emissions linked to agricultural activities are quantified and modelled using Life Cycle Assessment methods. You will be familiarised with cacao and coffee production systems and their broader environmental impacts. You will gain experience in conducting literature reviews, data mining and data analysis. You will also gain skills in Excel, Git and JSON schemas.

Entry requirements

You should have, or be studying, a degree in a quantitative discipline, such as Biology, Chemistry, Engineering, Environmental Science, Mathematics, Physics.

Biology 07
Signals and strategies: how fish communicate to manage cooperation and conflict

Primary supervisor

Dr Katie Dunkley

Project description

Fish live in social worlds where encounters with other species can bring rewards and/or risks. Some interactions benefit both sides, as when cleaner fish remove parasites, whereas others are harmful, involving species that feed on a fish’s mucus or scales. This project will investigate how fish use signals, such as body postures or chases, to influence the outcomes of these encounters in their favour. By analysing pre-collected video footage and/or conducting a laboratory experiment, it will explore how communication helps fish encourage cooperation, avoid harm, and navigate the challenges of living alongside other species. In doing so, the project will offer new insights into how animals use signals to manage conflict and cooperation, and how communication shapes social behaviour across species.

Project outcome

You will receive training in behavioural data collection and in how to code, analyse, and interpret animal signalling and interaction patterns using video and/or experimental data. You will also have the opportunity to develop practical skills in animal handling and fish husbandry within an aquatics laboratory. In addition, you will gain experience in designing decision-based experiments, formulating and testing hypotheses, and performing statistical analyses. There may also be opportunities to explore artificial intelligence approaches to data analysis.

By the end of the project, you will present a summary of your findings to other researchers in fish behaviour at an internal meeting. If your analysis contributes to a future publication, you will be included as a named co-author on that paper.

Entry requirements

You should be have, or be studying, a degree in a Biology-related discipline. A keen interest in behavioural ecology, communication, and/or social interactions in animals will be an advantage. Experience with data analysis in R and/or coding in Python would be helpful, but not essential as training and support will be provided.

Biology 09
Hangry flies: impact of food deprivation on female aggression

Primary supervisor

Dr Irem Sepil

Project description

Aggression is common across the animal kingdom and winning contests helps individuals secure food, mates, or territory - key resources for their survival and reproduction. However, whether an animal should fight, how long for, and what determines who wins are fundamental questions we are still trying to answer. Female aggression, compared to males, has historically been neglected but there is growing evidence of the evolutionary significance of female-female competition.

Internal states are likely to influence an individual’s capacity and motivation to fight. For example, hunger may decrease an individual’s ability to win fights yet simultaneously increase their valuation of food as a resource worth fighting over. Precisely how food deprivation affects aggression is unclear. This is especially relevant for females, who typically fight over food resources. Therefore, this project will focus on understanding how food deprivation influences aggression in females.

Project outcome

You will be trained in key laboratory skills working with the model species Drosophila melanogaster, including setting up aggression assays as well as general fruit fly husbandry. You will also be trained in supervised machine learning approaches for behavioural tracking and video analysis in order to quantify aggression, as well as data analysis in R.

At the end of the project, you will use these skills to write a report and present your findings to the group. Results from this project may be used in future publications, and you would be included as a named author on such work.

Entry requirements

You should have, or be studying, a degree in Biology or a related discipline. You should have a strong interest in animal behaviour, ecology, or evolution.

Biology 10
Assessing the biodiversity potential of schools as urban green spaces

Primary supervisor

Dr Joanna Bagniewska

Project description

School grounds have great potential for providing refuge for urban wildlife – yet are one of the most under-recorded urban environments in the UK. At the same time, there is a growing dissociation from nature, particularly notable among children and teenagers. While increasing numbers of schools are taking part in citizen science projects, such as National Education Nature Park, to map and monitor habitat and biodiversity on their grounds, not many of them have been assessed systematically by ecologists rather than citizen scientists.

This project aims to run biodiversity surveys across schools in Oxford to provide a baseline assessment, which can yield meaningful ecological data and be used as a benchmark for future outreach and public engagement projects. Information gained from this pilot will be shared with participating schools, as well as the Healthy Ecosystem Restoration in Oxfordshire network and the British Ecological Society.

Project outcome

By the end of the internship you will have:

1. gained experience in conducting biodiversity surveys across a range of taxonomic groups;
2. contributed to survey planning and setup; collected data contributing to a potential future publication, which you would be asked to co-author;
3. gained communication skills by presenting your findings to colleagues, teachers and students; and
4. gained project management skills and experience of working as part of a research team.

Entry requirements

You should have, or be studying, a Biology-related degree, and have an interest in ecology and conservation work. Some species identification skills would be very useful, although help will be provided in expanding these. You will be a self-starter with the ability to work independently.

You will be required to undergo a DBS check, since you will be working on school grounds.

Clinical Medicine 01
International health workforce recruitment in the UK

Primary supervisor

Dr Yingxi Zhao

Project description

The UK’s health and care services face major challenges in recruiting and retaining sufficient staff. Currently, one in five NHS or social care workers in England is internationally recruited, a figure that is expected to rise. Private recruitment agencies are playing an increasingly central role in facilitating the international migration of health workers, particularly nurses, against the backdrop of a global workforce shortage. This internship, building on work by the Health Systems Collaborative team on UK and global health workforce issues, will involve a series of literature, policy reviews, and web-based content analysis to map recent policy changes, identify key actors, and update the evidence base on the experiences of internationally recruited staff.

Project outcome

Through this project, you will develop skills in conducting comprehensive literature and policy reviews, learn how to design search strategies and apply thematic coding, and gain experience of working with an applied health research team and applying analytical frameworks and theories.

We will expect you to produce a report or other relevant output to disseminate the findings - and to present this to colleagues in the Health Systems Collaborative team. If any aspect of your analysis is included in a future publication, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a degree in Medicine, Allied Health, or Social Sciences (eg Education, Policy, Sociology, or Anthropology). Applicants from other disciplines are also welcome to apply, provided they can demonstrate a strong interest in social science or health services research, along with relevant skills such as literature and systematic reviews, and clear written and verbal communication.

Clinical Medicine 02
Evidence synthesis through existing literature to combat poverty-related infectious diseases

Primary supervisor

Dr Makoto Saito

Project description

Clinical care in poorer countries often lacks strong supporting evidence, even though infectious diseases are the leading cause of morbidity and mortality in many of these areas. As part of the Infectious Diseases Data Observatory (IDDO)’s aim of enabling evidence-based decision-making to combat infectious diseases, our current project uses systematic reviews of existing literature to gather evidence regarding infectious diseases of poverty (such as malaria and neglected tropical diseases) in low- and middle-income countries.

We have extensive experience of such work, and have streamlined the evidence-gathering process, with our work feeding into the development of numerous clinical guidelines, including the World Health Organization’s malaria guidelines. We seek individuals interested in understanding these topics and the process of evidence synthesis. Expected activities include reviewing medical literature and summarizing findings by extracting relevant data. You may also have opportunities to contribute to report writing based on these findings.

Project outcome

You will gain a deeper understanding of the context (malaria, neglected tropical diseases or antimicrobial resistance) and develop skills in critically appraising published literature, an essential first step for many scientific projects, including defining a postgraduate project which clearly addresses a knowledge gap regardless of whether it involves wet lab or dry lab work. Working in collaboration with our team, you will also gain an overview of how to conduct systematic literature reviews in a scientifically rigorous way and synthesise existing evidence.

Entry requirements

You should have, or be studying, a degree in Biology or Medical Sciences. You should have a basic understanding of medical terminology and be competent in reading medical articles. No prior experience in statistical analyses or data management is required, although it can be helpful. The project involves no wet-lab work and primarily requires computer-based work.

Clinical Medicine 03
Engineering synthetic proteins to suppress oncogenic signalling.

Primary supervisor

Dr Christina Heroven

Project description

Receptor Tyrosine Kinases (RTKs) are proteins on the surface of cells that act like molecular switches, controlling important processes such as cell growth, development, and specialization. In cancer, RTKs are often mutated or become more abundant, leading to uncontrolled cell growth. Due to their involvement in disease, RTKs have become major targets for drug development.

In this project, we aim to explore a new way to inhibit cancer-promoting RTK activity by using the cell’s own regulatory system. Specifically, we plan to design a synthetic protein that brings an enzyme called a phosphatase to the target RTK, helping to switch off its cancer-driving signals.

Project outcome

You will be trained in basic molecular cloning and biochemical techniques. You will learn how to design, express and purify synthetic recombinant proteins. You will test the synthetic proteins for their anti-oncogenic properties using cancer cell proliferation assays and flow cytometry. You will also have the opportunity to characterize the molecules by biophysical methods. At the end of the project you will present your findings back to the group in an internal meeting.

Entry requirements

You should have, or be studying, a degree in Molecular Biology, Biochemistry, Biophysics, or Biological Sciences.

Clinical Medicine 04
Microplastics and the connection with antimicrobial resistance in a hospital environment

Primary supervisor

Dr Aram Swinkels

Project description

This project brings together two major health challenges of the 21st century: antimicrobial resistance and the widespread presence of microplastics.

Microplastics are now found almost everywhere, even in unborn foetuses. While they are believed to negatively impact human health, they may paradoxically provide advantages to bacteria. Microplastics have been detected in hospital sink drains alongside healthcare-associated pathogens, raising the question: how do bacteria and microplastics interact within the hospital environment?

In this project, we will perform in-vitro experiments to investigate how different bacterial strains behave when attached to microplastics. Using a diverse selection of materials from our in-house microplastics library, we will create a mock bacterial community for inoculation. You will perform growth experiments combining this community with various microplastics and assess resistance related parameters using classical, molecular, and computational microbiology approaches.

Project outcome

During the project you will work with microbiologists and computer scientists, developing skills in microbiology, assay development and data analysis. The aim of the project will be to produce a summary scientific report on the findings of your experimental evaluation and present this in one of our research laboratory meetings. Depending on the work undertaken and findings this could also be submitted as a conference abstract and/or contribute to a scientific publication.

Entry requirements

You should have, or be studying, a degree in Microbiology or a related discipline. Previous laboratory experience in culture, DNA extraction and sequencing is desirable.

Clinical Medicine 05
Generating second-generation human lymphoid organoids as a model to study adaptive immune responses

Primary supervisor

Dr Delaney Dominey-Foy

Project description

This project will explore how human immune cells interact and develop within complex three-dimensional tissue models. Using advanced lymphoid organoids, which are miniaturised systems that mimic the structure and function of human lymphoid tissue, you will investigate how T and B cells coordinate adaptive immune responses. You will gain practical experience in human cell culture, microscopy, and data analysis, contributing to ongoing efforts to establish next-generation organoid models for studying human immunity.

Project outcome

You will gain hands-on experience in human tissue culture, including the generation and maintenance of advanced lymphoid organoids. You will be trained to use confocal microscopy to image immune cell interactions and to analyse these images using AI- and machine learning–based tools. You will also perform and interpret multicolour flow cytometry to identify and quantify immune cell populations.

Alongside these technical skills, you will develop your ability to handle, visualise, and statistically analyse complex biological datasets. You will also gain experience in presenting your findings, both in group meetings and in a final research presentation.

By the end of the project, you will have a deeper understanding of human immunology and how adaptive immune responses can be studied using next-generation organoid models. If your data contributes to a future publication, you may be acknowledged or included as a co-author.

Entry requirements

You should have, or be studying, a degree in a relevant Biological or Biomedical Science, including courses that contain content on immunology, cell biology, biochemistry, or a related field. An interest in human immunology and experimental biology is essential.

Previous laboratory experience is helpful but not required as full training will be provided. Confidence in handling data and a willingness to learn computational approaches (for example, image or flow cytometry analysis) would be advantageous.

You should have good organisational skills, attention to detail, and enjoy working collaboratively as part of a research team. Curiosity, motivation to learn new techniques, and an enthusiasm for experimental discovery are the most important qualities.

Clinical Neurosciences 01
Developing shark antibodies as biomarkers for peripheral nerve injury

Primary supervisor

Dr Alexander Davies

Project description

Neuropathic pain is a chronic condition that occurs because of injury or disease of the nervous system. It is thought to affect up to 10% of the adult population in the UK, yet diagnosis can be challenging. Identifying nerve damage in individuals with chronic pain would help tailor treatments and enhance our mechanistic understanding of the condition.

This project aims to develop small peptide molecules – shark antibodies - as biological tools to recognise injured sensory neurons in mice and humans. We will validate them as potential novel biomarkers for nerve injury.

You will be trained in wet-lab techniques relevant to the project at the time, which may include immunohistochemistry, enzyme-linked immunosorbent assay (ELISA), live cell-based assay and confocal microscopy.

In the longer term this work could help develop therapeutic interventions that target injured nerves specifically and avoid the side-effects of current medications for neuropathic pain.

Project outcome

You will be part of a small research team working in collaboration with an industrial partner employing proprietary technology to address this biological and therapeutic challenge. As well as being taught laboratory skills relevant to biological research more generally, you will learn how to design, execute and analyse research experiments. You will attend weekly lab and journal club meetings, as well as monthly project meetings with our industrial partner. At the end of your project you will have the opportunity to present your findings at a large group meeting.

Entry requirements

You should have, or be studying, a Biology or Biomedical Science-related degree with and have an interest in nervous system function in the context of health and disease. You will be able to input data into a spreadsheet and perform basic statistical analysis. You will appreciate the core principals of experimental design and scientific report writing. You will understand the basic principles of neural anatomy, antibody chemistry, and fluorescence microscopy. Experience in a biological laboratory is useful but not essential.

Clinical Neurosciences 02
Enhancing cognitive impairment detection and inclusivity in stroke care

Primary supervisor

Dr Sam Webb

Project description

Aim: There is no one assessment that can accurately flag impairments in thinking skills following a stroke that:

1. is suitable for any time post-stroke;
2. is suitable for different severity of strokes (mild to severe);
3. can account for different impairments in vision, attention; motor abilities, and communication; and
4. can be completed in a brief as time as possible.

We designed such a test in an app that does all of these things, and we are currently assessing its accuracy and validity in multiple stroke settings.

Method: Administering neuropsychological assessments to stroke survivors who live in the community (in and around Oxfordshire), scoring assessments, entering data, and brief data analyses. Training in test administration and data curation/analysis will be provided.
Project outcome

You will be trained in neuropsychological assessment methods and scoring, as well as in data analyses using R. You will have the opportunity to contribute to lab meetings and gatherings during your internship. You will also gain experience in a translational neuropsychology research group, learning about the different types of research conducted and future pathways.

Entry requirements

You should have, or be studying, a degree in Psychology or closely related field, such as Neuropsychology. This project would suit someone who is interested in studying and/or progressing into clinical (neuro)psychology.

Clinical Neurosciences 03
Modelling neurodevelopment with cerebellar organoids

Primary supervisor

Professor Esther Becker

Project description

This project aims to understand how the brain’s immune cells, called microglia, help shape the development of the cerebellum, a key brain area that is important for motor control but also cognitive and emotional processes. We are using human induced pluripotent stem cells (iPSCs) to generate cerebellar organoids and will investigate how microglia and cerebellar neurons interact in this novel model system. Interesting questions that could be explored in the project include:

• Do microglia influence the development and maturation of specific neuronal subtypes in the cerebellum?
• How does the cerebellar microenvironment shape the identity and function of co-cultured microglia?

Lab techniques to address these questions will include tissue culture, molecular techniques such as qRT-PCR, and immunostaining.

There might also be the possibility to undertake functional analyses using multi-electrode arrays.

Project outcome

You will gain experience in experimental design, tissue culture, molecular and imaging techniques, and data analysis. You will have regular meetings with your supervisors to discuss your project and plan next steps. At the end of the project, you will present your findings back to the group at our lab meeting.

Entry requirements

You should have, or be studying, a degree in Biomedical Sciences or equivalent, such as Biology, Biochemistry, or Pharmacology. An interest in neuroscience would be advantageous. Training in all required techniques will be provided, but knowledge of experimental techniques and wet-lab experience would be useful.

Clinical Neurosciences 04
Immunohistochemical characterization of LGI1-AAV in a neuropathic pain model.

Primary supervisor

Professor John Dawes

Project description

Neuropathic pain arises from damage to the nervous system and is associated with overactive neurons in the dorsal root ganglia (DRG) and spinal cord. Current treatments are often ineffective, highlighting the need for new approaches. Leucine-rich glioma-inactivated 1 (LGI1) is a secreted protein that regulates neuronal excitability (Fels et al., 2021). Autoantibodies against LGI1 have been linked to pain syndromes, and reducing these antibodies improves symptoms (Gadoth et al., 2017; Ramanathan et al., 2021). Using adeno-associated virus (AAV9) gene delivery, we increased LGI1 expression in a mouse model of nerve injury and found reduced pain sensitivity (Farah et al., 2024).

To understand how LGI1 works, we will use immunohistochemistry and confocal microscopy to study its expression and viral transduction in specific neuronal populations from spinal cord and dorsal root ganglia tissue. This project aims to identify LGI1’s mechanisms of action and evaluate its potential as a novel gene therapy for neuropathic pain.

Project outcome

You will gain hands-on experience in neuroscience and molecular biology techniques, focusing on neuropathic pain mechanisms. You will be trained to handle and analyse experimental data from immunohistochemistry, learning to identify specific neuronal populations using confocal microscopy. You will also learn the principles of viral vector design and gene delivery, as well as how to interpret and present quantitative results using appropriate statistical methods. Additionally, you will gain insight into how preclinical models are used to investigate therapeutic strategies for nervous system disorders. The intern will present their findings in a lab meeting at the end of the placement and, if their work contributes to future publications, may be acknowledged or included as a co-author.

Entry requirements

You should have, or be studying, a degree in Neuroscience, Biomedical Sciences, Physiology, Pharmacology, or a related discipline. An understanding of basic neurobiology and molecular or cellular biology techniques would be advantageous. Prior experience with laboratory work would be useful but is not essential, as full training will be provided. You should have strong organisational skills, attention to detail, and enthusiasm for experimental research. The ability to work carefully, follow protocols, and communicate findings clearly will be important for success in this project.

Clinical Neurosciences 05
Estimating sleep from wearable devices

Primary supervisor

Professor Simon Kyle

Project description

Wrist-worn actigraphy is commonly used in sleep research and clinical sleep medicine to estimate sleep and rest-activity rhythms. Actigraphy data are typically combined with patient-reported sleep diaries and manually scored by a clinician or researcher. However, the inclusion of actigraphy in large-scale cohort studies (involving 100,000+ participants) has led to the development of automated analysis approaches, enhancing efficiency and reliability. Nevertheless, it is unclear how sleep outcomes might differ between automated and manual scoring approaches.

In this project, we will explore how sleep parameters may differ when data are manually scored, with the aid of a sleep diary, compared to outcomes from automated scoring. You will set-up actigraphy devices, collect data using these devices, and then analyse how the data compares across scoring methods. You will have the opportunity to learn more about sleep research methods, how to score sleep data, and plan and implement your own data analysis plan.

Project outcome

You will have the opportunity to learn about a range of wearable and nearable technologies used within sleep research. The focus will be on analysing data from a wrist-worn actigraphy device, and investigating how sleep parameters differ across manual and automated scoring. You will also have the opportunity to learn about polysomnography, regarded as the ‘gold standard’ for sleep research, and learn how to set-up and score sleep polysomnography data.

You will be expected to explore a wide range of literature around actigraphy and its scoring, collect your own data, create and implement a statistical analysis plan, and write a report of what you find. Additionally, we would like you to present your findings at a lab meeting.

Entry requirements

You should have, or be studying, a degree in Neuroscience, Psychology, or a related discipline. This project will suit someone with an interest in sleep and/or wearable technologies. Experience with data analysis is beneficial, but support and training will be provided if needed.

Clinical Neurosciences 06
Record and analyse the spontaneous neuronal firing in a humanised live neuron recording system

Primary supervisor

Dr Pao-Sheng Chang

Project description

Neuropathic pain is affecting 9% of people worldwide, but many promising pre-clinical models fail to be translated into effective treatments. To better investigate the paroxysmal pain in human, we have developed a bespoke humanised live neuron recording system to measure the level of spontaneous firing in human sensory neuron. The system is applied to understand the causal relationship between co-culture cells or inflammatory mediators with human sensory neuron by measuring the level of spontaneous neuronal firing. We can then relate the cell-based measurements to the paroxysmal pain in clinics for developing better pain treatments.

Project outcome

You will experience live neuron recording conduction and analysis whilst also gaining the practical experience in maintaining human iPSC-derived sensory neuron in culture dish apart from other fundamental wet-lab practices. We aim to build up your experience and confidence in the lab with providing guidance for future study and career trajectory.

In addition, as part of a multidisciplinary pain research group working on projects from the bench to the clinic, this internship will also offer the unique opportunity to spend time each week with clinical researchers within the group. You will have the chance to observe data collection techniques used for investigating the mechanisms underlying painful conditions in patients, and learn more about translational research as a bonus.

Entry requirements

You should have, or be studying, a degree in biological science, preferably neuroscience. You should have an understanding of fundamental wet-lab works (such as cell culture and imaging technologies).

Clinical Neurosciences 07
Mapping global epilepsy research

Primary supervisor

Professor Arjune Sen

Project description

Epilepsy affects more than 50 million people worldwide, with a disproportionate burden in low- and middle-income countries where access to diagnosis and treatment remains limited. This project contributes to the development of a Global Epilepsy Research Database, which collates and categorises literature on epilepsy care including diagnosis, treatment and prevention.

Working very closely with colleagues in Medical Humanities and an interdisciplinary team across neuroscience, global health and clinical research, the project will also support the development of an associated blog platform that translates research findings into accessible, practice-relevant insights.

This internship will use literature review techniques and thematic analysis to map current evidence. Together, these activities will help highlight research priorities and support future work to strengthen epilepsy care in resource-limited settings. Projects will align with the interests of applicants and our current work on epilepsy in LMICs, culturally contextualised technology, medicines access and epilepsy in dementia.

Project outcome

You will gain experience in literature review, database organisation and science communications. You will also develop skills in interpreting and presenting research data. You will have the opportunity to contribute to outputs from the project, which may include online publications or scientific papers.

Entry requirements

You should have, or be studying, a degree in Neuroscience, Psychology, Medicine, Public or Global Health, Biomedical Sciences or a related discipline. Strong analytical and writing skills are essential. Familiarity with PubMed or Excel would be advantageous. An interest in neurological disorders and global health research is essential. You should be motivated, detail-oriented and able to work independently and collaboratively.

Medicine 01
Generative AI for materials science

Primary supervisor

Dr Najib Sharifi

Project description

This project aims to develop a framework that combines a Monte Carlo Tree Search (MCTS) algorithm with generative machine learning models to design new materials with desired chemical or physical properties. Through this project, we will explore how reinforcement learning and generative AI can accelerate the discovery of functional materials.

Project outcome

You will gain hands-on experience with machine learning for scientific discovery, including training and applying transformer and GNN models, and implementing search algorithms such as MCTS. You will also develop practical skills in Python programming, data handling, and computational chemistry tools, as well as experience interpreting model outputs in a research context. You will present your findings to the group and may contribute to a research preprint or publication depending on project progress.

Entry requirements

You should have, or be studying, a degree involving machine learning, such as Computer Science or a related discipline. You should have familiarity with writing machine learning models from scratch in pytorch. Knowledge of materials science is not necessary.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Medicine 02
Unconventional TCR evaluation

Primary supervisor

Dr Martin Lett

Project description

In our current project, we isolate T-cell receptor (TCR) sequences and clone them into cell lines to evaluate their antigen recognition, mainly within the non-classical MHC context. This work aims to deepen our understanding of antigen presentation to MAIT cells and to identify T-cell antigens that are specific to cancer.

Project outcome

You will gain hands-on experience in key immunological and cell biology techniques. You will learn how to perform in vitro T-cell activation assays and maintain mammalian cell cultures under controlled laboratory conditions. You will also be trained to use spectral flow cytometry to analyse cellular phenotypes and immune responses in detail. You will acquire skills in bioinformatic analysis to interpret complex cytometry datasets and integrate them with experimental findings.

Entry requirements

You should have, or be studying, a degree in a relevant discipline, such as Biology, Biomedical Sciences, Biochemistry, Pharmacology, or a related discipline and have a basic understanding of immunology, particularly T-cell biology and antigen presentation.

The ability to follow experimental protocols and maintain accurate labs is essential. Laboratory skills such as sterile cell culture techniques, pipetting, and handling biological samples would be advantageous, but is not a requirement. You should be motivated to learn new techniques and engage with interdisciplinary approached combining wet-lab and computational work.

Medicine 03
Investigating T–B cell interactions in vaccine responses using human tonsil organoids

Primary supervisor

Dr Nicholas Provine

Project description

Human tonsil organoids have recently emerged as a promising model for studying vaccine-induced immune responses in vitro. Our group has validated this system for modelling responses to the adenovirus-vectored vaccine ChAdOx1 nCoV-19, better known as the Oxford–AstraZeneca COVID-19 vaccine, and identified key cytokine pathways involved in B and T cell responses. The next step is to determine how well this model reproduces the interaction between T cells and B cells, which is essential for classical antibody production in vivo.

In this project, you will manipulate T cell function in the tonsil organoid system using specific antibodies and assess the effect on B cell activation and antibody secretion. Immune responses will be analysed using flow cytometry and ELISA. This work will advance understanding of the tonsil organoid model and refine its use for studying vaccine-induced immunity.

Project outcome

You will be trained in fundamental immunology techniques, including cell culture using primary human lymphoid cells, flow cytometry for characterising immune cell activation, and ELISA assays for measuring antigen-specific antibody responses. You will also gain hands-on experience in experimental design, data recording, and data interpretation.

Through this work, you will develop practical laboratory skills and a deeper understanding of vaccine immunology. You will be encouraged to present your findings in a group meeting, and if your data contributes to a future publication, you will be acknowledged as a named co-author.

Entry requirements

You should have, or be studying, a degree in a biological or biomedical science subject, such as Immunology, Microbiology, or Biochemistry. An interest in immunology or infectious diseases would be beneficial. You should have good attention to detail, be willing to learn new laboratory techniques, and have a careful and methodical approach to experimental work. Prior laboratory experience is not required, as full training will be provided.

Medicine 04
Decoding the premature gut: using spatial transcriptomics to gain insights into necrotising enterocolitis (dry lab)

Primary supervisor

Dr Agne Antanaviciute

Project description

Necrotising enterocolitis is a severe intestinal disease affecting premature infants, where parts of the gut become inflamed and die. Despite medical advances, the cause remains poorly understood, making it difficult to diagnose and treat. In severe cases, the disease can progress rapidly, leading to holes forming in the intestinal wall that require surgery.

The Antanaviciute and Simmons labs, based at the Weatherall Institute of Molecular Medicine, investigate intestinal development and disease using cutting-edge techniques such as spatial transcriptomics, which reveal how genes are active in different parts of the gut.

This project offers a rare opportunity to analyse high-dimensional spatial data from human neonatal tissue – an invaluable and limited resource. You will learn to analyse and identify cellular changes that drive disease progression in premature infants with Necrotising Enterocolitis, which may contribute to identifying early biomarkers to improve diagnosis and treatment.

Project outcome

You will receive training in computational tools used to analyse high-dimensional spatial transcriptomics data – a cutting-edge approach recognised by Nature Methods as 'Method of the Year' in 2020. Although still in its early stages, this technology holds immense potential for uncovering complex biological processes. You will work within an interdisciplinary team of biologists, clinicians and computational scientists, gaining a holistic understanding of gut development and disease and clinical research pipeline. While this is a dry lab project, there may be opportunities to observe or contribute to molecular biology experiments if desired. If any aspect of analysis is included in future publication, you may be included as a named co-author.

Entry requirements

You should have, or be studying, a degree in Biology, Biochemistry, Biomedical Science, or a related discipline. As it is a fully computational project, some experience in R would be an advantage.

Medicine 05
AI deep learning for clinical research

Primary supervisor

Professor Qiang Zhang

Project description

AI deep learning is transforming the world in many aspects, but its real-world clinical applications have been hindered by the knowledge gap between machine learning scientists and clinicians. We actively address this by developing AI algorithms next to clinical doctors at an interdisciplinary clinical research unit. We offer opportunities to work with a cross-disciplinary team to gain experience in developing deep-learning solutions for unmet clinical needs. This may include data pre-processing, neural network design, data analysis, and method validation.

You will have the chance to observe real-world clinical MR scans at the Division of Cardiovascular Medicine, and access to computing facilitates at Oxford Big Data Institute.

Project outcome

You will gain domain knowledge of both deep learning and cardiovascular imaging, skills in medical data processing, neural network design in Python, and valuable experience in developing AI algorithms in clinical research settings.
Entry requirements

You should have, or be studying, a degree in Computer Science or Engineering. You should have experience in machine learning and coding in Python.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Medicine 06
Developing a simulation framework for clinical hyperpolarized MRI experiments

Primary supervisor

Dr James Grist

Project description

This project will focus on developing a computer simulation that can be used to describe, and more importantly optimise, metabolic imaging experiments in humans. You'll use data already present in the lab, as well as fundamental physics mathematics, to make this framework as true to real life as possible. You'll use Python or Matlab to code the project and be well-supported in your time here.

Project outcome

You will be trained in fundamental MRI physics, python, Matlab, and image processing. You will have the opportunity to engage in lab meetings and experience and understand the work being performed by other members of the lab. You will be involved in research discussions and the day to day life of the lab. You'll be able to present your work at lab meetings and gain experience in scientific presentation.

If completed, you will be able to publish this work.

Entry requirements

You should have, or be studying, a degree in Computer Science, Engineering, or Physics. Experience in Matlap or Python is essential.

Medicine 07
Super-resolution imaging of cell adhesions in gut patient organoids

Primary supervisor

Dr Karina Pombo-Garcia

Project description

Our gut ages, which has implications for the cellular organization of proteins involved in cell-cell adhesion. Cell-cell adhesions are like the building blocks of a house, and the proteins responsible for holding them together act as the glue that allows our organs to function. In this project, you will have the opportunity to visualize, at very high resolution (on the nanometer scale), the distribution and organization of cell adhesion proteins in a gut-like model called gut-organoids. We will use patient-derived organoids from individuals of different ages to map how these proteins assemble as we age.

You will learn and use super-resolution microscopy with fluorescence proteins to visualize them in human tissue. Additionally, you will get familiar with handling the organoids and observing how they phenotypically change over several days in culture. While not required, it would be ideal if you have some basic knowledge of coding, such as Python, to assist in processing the images.

Project outcome

You will gain experience in several key cell biology techniques as well as being immersed in an active research environment. This will enable broadening of expertise, as well as help you derive a better understanding of studying and working in a research laboratory.

The project itself will allow for training in cell culture and aseptic techniques. You will also be trained on key super-resolution imaging microscopy STED, as well as gut organoid development and maintenance.

There will also be the opportunity to work in a lab with a range of experience from junior to senior scientists who are willing to advise and mentor. If successful, the data and resources generated will have a real-world impact on ongoing research with your contribution credited accordingly if work with their direct involvement is published.

Entry requirements

You should have, or be studying, a degree in Biology, Biochemistry, or Medicine. Basic understanding of cell biology and microscopy, alongside familiarity with Python, would be advantageous.

Medicine 08
The extracellular vesicle profile of inherited cardiomyopathy

Primary supervisor

Professor Christopher Toepfer

Project description

Inherited cardiomyopathy affects 1:200 people. These conditions cause changes in heart function that lead to heart failure and sudden cardiac death. There are still many things to be understood about how these conditions drive the progress towards heart failure. A key signature that has yet to be studied is how extracellular vesicle (EV) release is altered in cardiomyopathy. We have the opportunity to use human stem cell derived cardiac cells that carry cardiomyopathy gene mutations to study changes in EV synthesis and release in the dish. This will inform our understanding of how these EV signatures of communication affect disease progression.

Project outcome

You will measure EV abundance and characteristics that will be extracted from media that human stem cell derived cardiomyocytes are grown in. You will be able to assess differences between control cell EV profiles and those of cells carrying cardiomyopathy genes. At the end of the project you will have the opportunity to present the work that you have performed to the research group. If any aspect of your analysis is included in a further publication you may be included as a named co-author on that publication.

Specifically you will have the opportunity to work with stem cells a their use in producing cardiac cells of the heart. You will gain experience with EV extraction protocols and assays for assessing EV abundance and size.

Entry requirements

You should have, or be studying, a degree in Biology, Biomedical Science, or Biochemistry and have experience or an interest in wet lab science.

Medicine 09
Investigating how sugars effect liver fat cell metabolism and risk factors for cardiovascular disease.

Primary supervisor

Professor Leanne Hodson

Project description

Liver disease, known as metabolic dysfunction-associated steatotic liver disease (MASLD) is an independent risk factor for heart disease. It is known that if you gain weight or have a diet high in saturated fat this can increase the risk of heart disease. It has emerged that having a diet where you don’t gain weight but is high in sugar, and low in saturated fat and alcohol, also increases a person’s risk for heart disease. It is unclear why this is but has been suggested to be due to a change in liver metabolism with the liver making particles that are more atherogenic.

To test this, this project will use well characterised in vitro cellular models that allow the exploration of how different amounts of sugar and fats alter the regulation of pathways that produce lipid-containing particles. This will be achieved by using cell culture techniques along with a combination of approaches, including, culturing cells with stable-isotope tracers (specially labelled molecules), and analysing cells and media using mass-spectrometry to determine how specific pathways, like fat synthesis are changed when different amounts of sugars are added, along with the analysis of molecules in the media using a biochemical analyser, and measuring changes in the expression of proteins (through western blots and PCR) in hepatocytes.

Project outcome

You will have the opportunity to gain experience and skills in working with specialised laboratory techniques that use stable-isotope tracers to follow the fate of sugars through metabolic pathways in liver cells and changes in biomarkers associated with cardiovascular disease. You will learn how undertake statistical test to determine if there are differences in how specific nutrients are metabolised in cell and there is the opportunity to contribute to other on-going experiments in the lab.

At the end of your project you will present your project and findings to the group at an internal lab meeting. If any of the findings you generate are included in a future manuscript, then you may be included as a named co-author on the paper.

Entry requirements

You should have, or be studying, a degree in Biochemistry or Physiology and have an understanding of the key metabolic pathways in humans.

NDORMS 01
Are regulatory T cells important during infection?

Primary supervisor

Professor Audrey Gerard

Project description

This year's Nobel Prize in Medicine was awarded to the discovery of regulatory T cells (Tregs). Tregs are cells of the immune system that are making sure our body does not attack itself. They mainly do so by restraining other immune cells, called conventional T cells. Conventional T cells are important to fight infection, but they also have the capacity to attack our own body. Without Tregs, conventional T cells would go uncontrolled and attack our organs, leading to autoimmunity.

In the Gerard lab, we aim to understand how Tregs control T cells not to attack our self, while allowing them to fight infection. To do so, we study where both Tregs and conventional T cells are found and how they influence each other during infection. We believe that Tregs are segregated from the site of infection, which is important to allow conventional T cells that can recognise infection to get activated and kill infected cells.

In this project, you will shadow and participate to our effort to characterise the location and communication between Tregs and conventional T cells during infection using techniques such as imaging or flow cytometry. You will also learn how to generate Tregs.

Project outcome

You will learn cell culture and imaging and get a deeper understanding of the immune system.

Entry requirements

You should have, or be studying, a degree in Medicine, Biology, or Immunology.

NDORMS 02
Systems biology of pain in chronic human inflammatory disease

Primary supervisor

Professor Mark Coles

Project description

This project will involve spatial transcriptomics of biopsies from patients with immune mediated inflammatory disease and relating the spatial gene expression to pain scores. The techniques include the application of a python package to analyse cellular segmentation and quantify the gene transcription on a single cell basis and then to use this information to quantify the structures in the tissues. The project will involve learning python and other data analytical techniques. The project aims to identify cells within the tissue biopsy, quantify gene expression in individual cells and then use this information to develop a map of cellular organisation and function and how this relates to pain.

Project outcome

You will undertake spatial data analysis of human tissue sections from inflammatory disease using with the aim of generating evidence for molecular pathways that might related to patient measured pain. The analysis will identify key cell types involved in pain and the phenotype of those cell types comparing patients with high pain to controls.

Entry requirements

You should have, or be studying, a degree in Mathematics, Physics, Computer Science, or another relevant discipline, and be interested in applying your skillset to a biological problem.

NDORMS 03
Real-world evidence on the safety and effectiveness of novel medicines in cardiovascular and metabolic diseases: A systematic review

Primary supervisor

Dr Annika Jödicke

Project description

Understanding how well the new medicines for cardiovascular and metabolic disease work, and how safe they are in real-world settings is essential. This project will focus on 'real-world evidence' (RWE), referring to studies using data gathered in routine care settings, such as by general practitioners in primary care, or in specialist settings of hospitals. Systematic reviews of the literature provide a comprehensive overview of the published knowledge at a time. This is particularly important to identify gaps for future.

You will begin by developing a protocol for a literature review, including a search strategy and criteria for including or excluding studies. With guidance from supervisors, you will then review and discuss the identified studies with the supervisor team, and write a summary of the findings that will inform future research. This work will provide valuable experience in research methodology, with the potential to published as part of a project report or scientific manuscript.

Project outcome

You will have the opportunity to gain experience in conducting literature reviews using a systematic search strategy. We will support you in developing a protocol for the review, work with specialists at the Bodleian Libraries to develop a search strategy, teach you how to screen titles and abstract, and extract relevant information from the scientific publications we shortlist.

We aim to write a report on the literature review that will be published alongside the protocol, and, where possible, include the findings from the literature review for the introduction/discussion sections of future research publications or project reports.

Entry requirements

You should have, or be studying, a degree in a medical-related field, eg Public Health, Nursing, Pharmacy, Epidemiology, Medicine, or Statistics. An interest in reading medical literature and are interested in learning how to conduct literature reviews would be advantageous.

NDORMS 04
Bridging clinical trials and real-world data: Replicating and characterising clinical trial population from real-world healthcare databases

Primary supervisor

Dr Martí Català

Project description

Join a dynamic and multidisciplinary team in the Health Data Sciences group at NDORMS, University of Oxford. Our team comprises experts from various fields, including medicine, pharmacy, mathematics, statistics, and computer science, who collaborate to use real-world data (RWD) from hospitals, general practices, and disease registries to improve public health.

While essential for testing new treatments, clinical trials are conducted in controlled settings with carefully selected patient populations. This limits their ability to fully represent how treatments perform in the real world. By contrast, RWD reflects the broader population and real healthcare practices, offering insights that clinical trials might miss, such as unobserved side effects or how treatments are used in routine care. However, since RWD was not originally collected for research purposes, careful processing and analysis are required to ensure reliable scientific evidence.

In this internship, you will reproduce and characterise (such as analysing the survival of patients with a specific disease or comparing the effectiveness of a drug) a population of interest using RWD sources, including data collected from general practices, to answer research questions.

Project outcome

This internship provides comprehensive training in R programming, big data handling, clinical epidemiology, and biostatistics. You will have the opportunity to build your presentation skills by regularly presenting your work in team meetings, with potential for co-authorship on project publications. These skills are highly valued in both academia and industry (particular in pharmaceutical related fields), providing a solid foundation for future job applications and career development.

Entry requirements

You should have, or be studying, a STEM-related degree. Familiarity with R programming is desirable.

NDORMS 05
Uncovering how genetic variation shapes gene activity in single cells

Primary supervisor

Dr Yang Luo

Project description

This project explores how genetic variation influences gene activity at single-cell resolution. Using data from a newly generated single-cell study, you will investigate how genetic differences shape gene regulation across cell types. Possible directions include visualising genetic associations, linking variants to regulatory mechanisms, or applying simple AI or machine-learning approaches to identify patterns in the data.

The project will use computational and bioinformatics tools such as R, Python, and online genomic databases. It offers hands-on experience with real genomic data and introduces how AI-driven analysis can enhance our understanding of gene regulation and its role in human disease.

Project outcome

You will gain hands-on experience working with genomic and single-cell data, using tools such as R, Python, and online databases for data analysis and visualisation. You will develop practical skills in bioinformatics workflows, data integration, and the use of basic AI or machine-learning methods to identify patterns in complex datasets.

By the end of the project, you will understand how genetic variation influences gene activity and feel confident working with large-scale biological data. You will present your findings to the research group, and if your analysis contributes to ongoing work, you may be acknowledged or included as a co-author in a future publication.

Entry requirements

You should have, or be studying, a degree in Mathematics, Biology or a related discipline. You should have a strong interest in genetics, genomics, or computational biology (although detailed knowledge of genomics or single-cell analysis is not required). Prior experience with programming or data analysis (for example, R, Python, or a similar language) would be advantageous. This project is ideal for students who are comfortable working with data and keen to apply or develop their coding skills in a biological research setting.

NDORMS 06
Developing an ultrasound-mediated nanoparticle drug delivery platform for treating bone metastases

Primary supervisor

Dr Dario Carugo

Project description

Bone metastases from a primary tumour are one of the most common sites of metastases, yet exhibiting high rates of mortality and morbidity. Patients suffer from cancer-induced bone pain, limited mobility, debilitating fractures, depression, cardiac arrhythmias and have an increased caregiving burden with reduced quality of life. Current methods of treatment are limited in improving outcomes and the lived experience of these patients.

The aim of the project is to investigate the efficacy of combining ultrasound-responsive nanoparticles with chemotherapeutic or targeted anticancer agents in vitro, using relevant bone-metastatic cancer cell lines. These particles start oscillating when exposed to ultrasound, which helps releasing a therapeutic agent at the desired location and point in time. Key techniques employed will include microbubble synthesis and characterisation, nanoparticle conjugation, particle sizing, microscopy, and cell culture–based cytotoxicity assays to identify drug targets. Fundamental data analysis and plotting will be utilised to assess efficacy.

Project outcome

You will gain practical, hands-on experience across both engineering and biological research disciplines. You will develop an understanding of nanoparticle and microbubble fabrication and characterisation techniques, as well as an understanding of mammalian cell culture and cytotoxicity assays to evaluate therapeutic efficacy. You will generate and analyse datasets evaluating whether the developed nanoparticle/microbubble formulations induce biological effects on cells. You will have the opportunity to present your findings to a multidisciplinary research group in an internal seminar and discuss your experimental results in the context of ongoing work. If your data or analysis contributes to a future publication or conference proceeding, you will be included as a named author.

Entry requirements

You should have, or be studying, a degree in a scientific or engineering discipline, such as Biology, Biomedical Sciences, Engineering, or a related discipline. Prior experience with, or understanding of, cell culture and basic microscopy techniques would be advantageous, but is not necessary. An interest in biomedical device development, cancer therapeutics, or drug delivery systems is desirable.

NDORMS 07
The role of vascular macrophages in health and disease

Primary supervisor

Professor Claudia Monaco

Project description

Macrophages have a key role in health and disease, by supporting organ function and immune responses to damage and disease. In homeostasis, ontogeny and organ-specific signals influence the phenotype and function of macrophages by activation of specific transcription factors. Using single cell (sc) RNA sequencing (RNAseq) and mass cytometry (CYTOF) in human and murine arteries, we revealed the existence of significant transcriptional and spatial heterogeneity of vascular and perivascular macrophages at the steady state. Distinct subsets are identifiable within the intima and adventitia of the arterial vessels. The functional and physiological relevance of this heterogeneity is unclear, but it points to different physiological functions related to immunity and metabolism. We developed genetically modified strains designed to evaluate the role of specific macrophage subsets in whole organisms and their origin, and state of the art imaging techniques to interrogate their intercellular interaction.

Project outcome

You will analyse data from disease relevant and combined models, human biobanks, combined with spatial proteomics technologies such as mass cytometry (and the relevant computational tools) to map the local interactions of vascular macrophages, immune and stromal cells and how they contribute to vascular health.

By completing this project, you will gain insight into the acquisition and analysis of single cell biology datasets from murine and human arteries and validation of key selective macrophage markers for identification of macrophage geography in arteries.

Entry requirements

This project, dependant on the successful candidate’s interests, may be computational or wet lab based and all necessary training will be provided. For a computer-based project, you should have, or be studying a degree in Computer Science or a related discipline. For a wet lab-based project, you should have, or be studying, a degree in Biology, Immunology, or a relevant discipline.

Paediatrics 01
Characterising vaccine-induced immune responses to outbreak pathogens

Primary supervisor

Professor Teresa Lambe

Project description

Vaccines are among the most effective and cost-efficient public health interventions, protecting millions from infectious diseases each year. Establishing protective immune responses and the formation of adaptive immune memory following vaccination are crucial for ensuring long-lasting immunity. Professor Teresa Lambe’s research group at the Oxford Vaccine Group is currently developing and testing vaccines against several high-priority outbreak pathogens, including Ebola virus, Marburg virus, Crimean-Congo haemorrhagic fever virus, and coronaviruses.

This project offers an exciting opportunity for a student to work alongside senior researchers in the field to characterise vaccine-induced immune responses using key immune monitoring techniques such as ELISpot, ELISA and viral pseudoneutralisation assays to assess T cell and antibody responses to an outbreak pathogen. The findings will contribute to understanding vaccine immunogenicity and guide the design of effective vaccines for future outbreak threats.

Project outcome

You will be trained in general laboratory practices and immunological assays, as well as in assay-specific data analysis. At the end of the project, you will have the opportunity to present your findings to the group in an internal meeting. Through this experience, you will gain valuable insight into the vaccine development pipeline and the assessment of immune responses, while working alongside experienced researchers in vaccinology. This placement will strengthen your technical expertise, communication skills and ability to collaborate effectively in an academic research environment.

Entry requirements

You should have, or be studying, a degree in Biology or Biomedical Sciences, and have an interest in infectious diseases.

Paediatrics 02
Testing new therapies for children's cancers

Primary supervisor

Dr Anna Rose

Project description

Our work studies the molecular pathways that drive cancers that affect children and young people, with a particular focus on brain and bone tumours. These cancers all have very poor outcomes, and currently there are no targeted therapies available. In our lab, we aim to understand the molecular pathways better, so that we can identify, design and test new potential therapies. In this project, you will test a range of repurposed and/or novel small molecule drugs in either bone or brain cancer cell lines. You will learn key skills in tissue culture (growing cell lines in the lab) and cell viability assays, which monitor the effectiveness of a drug. You will also gain exposure to a wide range of other molecular techniques, such as DNA, telomere and protein analysis. The project is ideal for someone interested in translational molecular biology.

Project outcome

You will develop skills in growing and testing a variety of cancer cell lines in tissue culture, as well as treating cells with chemotherapeutic agents. You will also learn techniques for measuring cell survival, such as MTT assay and clonogenic assay. This will include how to collect and analyse the data, including statistical analysis using appropriate software. You'll observe and assist with a variety of molecular biology experiments which are key to our research programme, including our labs special skills in telomere analysis. You'll attend our weekly lab meeting, which will include journal club presentations (where you can begin to learn about critical appraisal of published work) and data presentation - which we will support you in presenting at. If any aspect of your analysis is included in a future publication or conference presentation, you would be included as a named co-author on the work.

Entry requirements

You should have, or be studying, a degree in Biology or Biomedical Sciences, have an understanding of basic molecular concepts, and have an interest in translational cancer biology. Basic experience in laboratory wet work (eg use of pipettes and other basic equipment) is preferable but not essential.

Paediatrics 03
A study of the influence of lymphocyte dynamics on the outcomes of measles infection and vaccination

Primary supervisor

Dr Anet Jorim Norbert Anelone

Project description

Measles outbreaks are tragic reminders that it is important to be vaccinated against measles to reduce the risk of infection, health complications, and death. Measles infects immune cells, predominantly B and T cells, and these cells themselves are subject to variation due to age or other infections such as HIV. It is of public health importance to advance understanding of factors influencing successful vaccination, eliciting a high level of measles-specific neutralizing antibodies.

This study will investigate the impact of age, HIV status, and immune cell counts on the outcomes of measles vaccination. In particular, this study will involve mathematical modelling and statistical analysis to estimate how age-dependent changes in the counts of different subsets of T and B cells influence the number and kinetics of measles-specific antibodies.

The results have the potential to inform decision-making for the administration of the first dose of measles-containing vaccines.

Project outcome

You will gain hands-on skills and coaching in scientific programming in R, as well as in the mathematical and statistical analysis of experimental and clinical data.

You will also gain knowledge of Good Clinical Practice (GCP), Information Security and Data Protection, as well as the standard operating procedures of the Oxford Vaccine Group, including those related to statistical analyses for clinical trials.

You will also have the opportunity to attend the weekly meeting of statisticians at the Oxford Vaccine Group, and present at the end of your internship. We would be pleased to include you as a co-author or acknowledge your contribution in a future publication, should any of your work with us be incorporated. You will also be invited to write a personal reflection on your experience, which may be published on the website or in the newsletter of the Oxford Vaccine Group.

Entry requirements

You should have knowledge or experience in at least one of the following areas: scientific programming (R, Python, Stata, SAS, Matlab, etc.), statistics, epidemiology, clinical trials, mathematics, machine learning, control engineering, bioinformatics, data science, infectious diseases, computational biology, vaccinology, immunology, biology, medicine, or any other relevant discipline.

Paediatrics 04
Mapping the molecular landscape of the hypothalamus in autism through single-cell multiomics

Primary supervisor

Professor Stephan Sanders

Project description

This project explores how gene activity and regulation differ in the hypothalamus between autistic and non-autistic individuals using single-nuclei multiomic data, where both RNA expression and chromatin accessibility (ATAC) are measured from the same nuclei. The dataset includes 40 human donors with whole-genome sequencing, allowing us to explore how genetic variation influences transcriptional and regulatory networks.

In this project, you will identify cell types and pathways showing altered transcription or regulation, and relate these to genetic risk for autism. You will compare your findings with bulk RNA-seq from the prefrontal cortex (PFC), single-cell data from other brain regions (PFC, putamen, and Wernicke’s area), and published datasets. Recent work has shown that autism-associated genes are highly expressed in the developing hypothalamus and thalamus; this study will test whether similar or distinct molecular patterns occur in the hypothalamus, offering new insight into how gene regulation in the brain may shape neurodevelopmental differences.

Project outcome

You will be trained in cutting-edge statistical, bioinformatic, and genetic techniques for analysing single-nucleus multiomic data, integrating RNA expression, chromatin accessibility, and genetic variation. You will gain experience using computational tools to explore how these molecular layers interact and differ across cell types in the human brain. The project offers the opportunity to work alongside leading experts in autism genetics and neurogenomics, within a research environment also focused on developing therapies for rare disorders. At the end of the project, you will present your findings to the group in an internal meeting. If your analysis contributes to future publications, you may be acknowledged as a named co-author.

Entry requirements

You should have, or be studying, a degree in Natural or Life Sciences, Statistics, Mathematics, Computer Science, or a related discipline. Familiarity with R and/or Python is required, and experience with data analysis or visualisation would be advantageous. An interest in genomics, neuroscience, or gene regulation would be beneficial, as the project involves interpreting biological data in a quantitative context. You should be motivated to learn new computational approaches, have good attention to detail, and be comfortable working independently as well as collaboratively within a research team.

Pathology 01
Mechanisms of protein degradation

Primary supervisor

Professor Pedro Carvalho

Project description

Accumulation of misfolded proteins and aberrant protein aggregates are hallmarks of a wide range of pathologies such as neurodegenerative diseases and cancer. Under normal conditions, these potentially toxic protein species are kept at low levels due to a variety of quality control mechanisms that detect and selectively promote their degradation. Our lab investigates these protein quality control processes with a particular focus on ER-associated degradation (ERAD), that looks after membrane and secreted proteins.

The ERAD pathway is evolutionarily conserved and in mammals, targets thousands of proteins influencing a wide range of cellular processes, from lipid homeostasis and stress responses to cell signalling and communication. We investigate the mechanisms of ERAD using multidisciplinary approaches both in human and yeast cells. We are using CRISPR-based genome-wide genetic screens and light microscopy experiments to identify and characterize molecular components involved in the degradation of disease-relevant toxic proteins. In parallel, we use biochemical and structural approaches to dissect mechanistically the various steps of the ERAD pathways.

These strategies helped us in discovering ERAD mechanisms contributing to the homeostasis of the endoplasmic reticulum, the organization of the nuclear envelope and regulation of lipid metabolism. Although we focus primarily on fundamental aspects of protein quality control, our work will shed light on how these processes are disrupted in human disease and may ultimately contribute to better therapeutics.

Project outcome

It should be possible to characterize a new effector or substrate of the ERAD pathway using biochemical and/or genetic tools.

Entry requirements

A good background in Biology is desired, therefore, applicants must have completed at least an A-Level in Biology to be eligible for this project.

Pathology 02
How cells build complicated protein machines

Primary supervisor

Professor Jordan Raff

Project description

Centrosomes are complicated protein machines that play an important part in organising eukaryotic cells. If human cells lose their centrosome, they usually kill themselves, and centrosome dysfunction has been linked to a plethora of human diseases - including cancer and microcephaly. Almost all cells are born with a single centrosome that grows and divides; when the cell divides, each daughter inherits one centrosome and the cycle starts again.

This project involves using sophisticated microscopes to make movies of living cells expressing fluorescently-tagged versions of the key proteins that drive centrosome assembly. These large imaging datasets will be analysed using computational methods to track how the centrosomes grow and divide and how individual proteins behave. These quantitative measurements are allowing us to better understand how centrosome growth and division are regulated during cell division and development, providing important insight into how these processes go wrong in disease.

Project outcome

You will be trained in Drosophila genetics (setting up crosses to generate living fly embryos for analysis and injection), advanced microscopy (using several different types of sophisticated microscopes), and you will learn how to analyse large imaging datasets with various computational tools. You will also have a chance to learn some molecular biology (DNA cloning, mRNA preparation) and biochemistry (protein purification). You will attend our weekly group meetings and be expected to present your work to the Group at the end of your Project.

Entry requirements

You should have, or be studying, a degree in a relevant discipline, such as Biological or Physical Sciences, Mathematics, or Engineering. An interest in understanding how complex biological systems work is essential. All training will be provided.

Pathology 03
Developing a platform to study antigen-specific naïve T cells

Primary supervisor

Professor Omer Dushek

Project description

T cells are important white blood cells that orchestrate immune responses. They use specialised receptors, called T cell receptors (TCRs), to recognise specific molecular signatures on these targets. Understanding how T cells make these recognition decisions is crucial for improving vaccines and immunotherapies.

This project aims to develop a new system to study how naïve (unactivated) human T cells respond when they are given defined TCRs. You will introduce selected receptors and other cell signalling molecules into T cells using a technique called nucleofection and then measure how the cells react using cell culture, flow cytometry, and molecular biology approaches. The goal is to create a reliable platform to study early T cell activation, helping us understand how immune responses begin and how they could be better controlled in disease and therapy.

Project outcome

You will be trained in and have the opportunity to conduct molecular cloning, eukaryotic cell culture, and flow cytometry experiments. You will also gain experience in analysing flow cytometry data, gel images and fitting simple mathematical models to your data. You will produce figures of your data and present your findings at a friendly internal group meeting at the end of the project. The research findings may be included in a future research study.

Entry requirements

You should have, or be studying, a degree in Biochemistry, Molecular or Cellular Biology, or a related biomedical area; and have an interest in molecular immunology research.

Pathology 04
Processing of Alzheimer’s-associated protein aggregates by microglia

Primary supervisor

Dr Sally Cowley

Project description

Microglia are strongly implicated in the progression of Alzheimer’s, and neuroinflammation is also a feature of other neurodegenerative diseases, including Parkinson’s and ALS. We have developed a genetically tractable system for differentiating authentic human microglia from induced Pluripotent Stem cells, which is used widely to investigate disease pathogenesis and identify new therapeutic targets.

You will join our team who are using human iPS-microglia to help investigate how the Alzheimer’s-associated aggregation-prone protein tau is taken up by microglia, how it is trafficked in these cells, and how it is released from microglia in potentially more toxic forms, including in extracellular vesicles. You will focus on one aspect of this pathway, applying relevant assays to, for example, quantify tau in specific subcellular locations.

Project outcome

You will learn human iPS cell culture and differentiation, isolation of subcellular fractions and/or extracellular vesicles, immunocytochemistry with associated image analysis, and cellular assays for detection of tau.

Entry requirements

You should have, or be studying, a degree related to Biomedical Sciences. Knowledge of neuroscience and/or immunology would be advantageous. Familiarity with tissue culture techniques would be useful but is not essential.

Pathology 05
Investigating how molecular motors transport cargoes in cells using genome editing and microscopy

Primary supervisor

Dr Anthony Roberts

Project description

The goal of this project is to investigate how motor proteins transport cargo in eukaryotic cells, while obtaining training and experience in a variety of molecular and cell biology techniques. Our research group specialises in the motor proteins kinesin and dynein, which use ATP hydrolysis to move along microtubules. These motors transport a range of macromolecular cargo in diverse physiological processes, underscored by the severe human disorders that arise from their dysfunction.

To better understand their mechanisms of movement and regulation, we solve cryo-EM structures of kinesin and dynein complexes. From these structures we generate hypotheses, which we test by designing and introducing mutations into cultured cells using CRISPR-Cas9 genome editing. We then visualise the movement of the motors and their cargoes in mutant and wild-type cells, tagging the proteins with bright fluorescent proteins and observing their motility using advanced fluorescence microscopy. In this project, you will use these methods to generate a novel mutant and characterise its behaviour.

Project outcome

You will be trained in and have the opportunity to conduct molecular cloning, eukaryotic cell culture, and fluorescence microscopy experiments. You will gain experience in analysing sequencing data, gel images, and fluorescence microscopy time-lapse videos. You will produce figures of your data and present your findings at a friendly internal group meeting at the end of the project.

Entry requirements

You should have, or be studying, a degree in Biochemistry, Molecular or Cellular Biology, or a related discipline.

Pathology 06
Membrane proteins and signalling in health and disease

Primary supervisor

Professor Matthew Freeman

Project description

We study membrane proteins and how they control signalling and cellular responses to stress. These processes are implicated in multiple human diseases including cancer, neurodegeneration, inflammation and infection so, although we mostly do discovery science, our work has wide potential medical relevance, and we are also interested in the translational opportunities.

Our particular focus is the rhomboid-like superfamily. We were the first to discover rhomboids, and we proved that they were novel intramembrane proteases, conserved across evolution, and that they controlled growth factor signalling.

More recently we have become interested in the much wider superfamily of rhomboid-like proteins, the majority of which are not proteases. Of these non-protease rhomboid-like proteins, we especially focus on the iRhoms, which we discovered to be primary regulators of inflammation.

Our experimental approaches include genetics, cell biology, biochemistry and structural biology, mainly in mammalian cells but also with a variety of model systems.

Project outcome

You will gain experience with experimental cell and molecular biology, as well as participating in a research group focused on discovery science. By the end of the project, you should have completed some aspect of one of our projects. If what you do is included in a future publication you may be included as a co-author.

Entry requirements

You should have, or be studying, a mechanistic bioscience-related degree, such as Biochemistry, Biomedical Science, or Biology.

PCHS 01
How can cafeterias increase plant-based sales? An online study looking at different menu options

Primary supervisor

Dr Emma Garnett

Project description

Moving towards plant-based diets is essential to mitigate climate change and improve population health.

Our group’s previous research has found that people are more likely to choose plant-based options when these are 'unmatched' (eg falafel burgers vs chicken pies), rather than 'matched' (eg falafel burgers vs beef burgers) This could be because unmatched plant-based dishes have flavour and texture attributes that meat-eaters prefer (eg a meat-eater who dislikes pastry might choose a falafel burger over a chicken pie).

However, it can be challenging for canteens to serve unmatched meals which are extremely different and may have different sides (eg chips vs rice). Therefore, it is important to discover how 'unmatched' meals need to be to drive these effects. Eg is a korma sufficiently different from a Balti, or a lasagne from spaghetti?

In this internship you will help us set up an online randomised control trial to answer these questions.

Project outcome

You will be trained in setting up online randomised control trials – which is a common research method across many different disciplines. You will learn about setting up online surveys using the platform Qualtrics, data collection in online studies and initial data analyses. You will learn more about the health and environmental impacts of different diets and effective policies to change behaviour.

Entry requirements

You should have, or be studying, a degree in Medical Sciences, Biology, Geography, Psychology or another relevant discipline. You will be familiar with basic statistical analyses and the principles behind Randomised Controlled Trials. You will have a keen interest in sustainability, climate change mitigation and the food system. Some familiarity with the statistical platform R would be useful but not essential.

PCHS 02
Does night-time temperature have an impact on A&E visits for psychiatric conditions?

Primary supervisor

Dr Patrick Fahr

Project description

Ambient temperature has been associated with numerous adverse health outcomes and mortality, with the UN Environment Program stating that extreme heat is now the deadliest climate threat. This project will look at the impacts of night-time temperature on psychiatric visits to A&E in England. Research indicates increased risk for A&E visits with warmer daytime temperatures, however, mechanisms behind this association are still not well understood. A common theory that that poorer sleep quality on warmer days results in increased stress and anxiety, which could exacerbate mental health conditions – but is the association with daytime temperatures, or night-time temperatures? A few research papers have evaluated night-time temperature associations and psychiatric outcomes, but none of the studies have been in the English context.

This project aims to evaluate the association between night-time temperature and A&E visits for psychiatric conditions. The project may also evaluate this association with daily temperature range, the difference between the maximum and minimum daily temperature, typically corresponding to the daytime and night-time temperatures.

Project outcome

You will have the opportunity to gain experience working with a large, national health dataset to evaluate health outcomes using epidemiological methods. You will be exposed to different statistical and epidemiological methods, with the greatest focus on time series analyses. The primary goal of this project is to have you complete a data analysis on our dataset that will be presented to our research group at the end of the internship. This project may also lead to publication, although this may require some continuation of the project beyond the internship period.

Entry requirements

You should have, or be studying, a Health Science, Data Science, or Statistics related degree, such as Medicine, Biology, Public Health, Population Health, Health and Medical Sciences, Applied Mathematics with Statistics, or Statistics. An understanding of statistics and familiarity with R are required for this project.

PCHS 03
The interactional management of risk in urgent primary care contacts

Primary supervisor

Dr Rebecca Barnes

Project description

You will contribute to an ongoing study led by the primary supervisor of the interactional management of patient risk in urgent primary care contacts. You will be involved in the management and analysis of a unique dataset of recordings and transcripts of real clinical assessments. The aim of this project will be to identify and code instances in the data where clinical risk is being managed, such as when worsening advice is given.

Project outcome

Skills developed will include managing sensitive data, health communication research methods and basic descriptive statistics. You will also gain knowledge of the organisation and delivery of urgent primary care services, plus experience of working as a member of our friendly research team.

At the end of the project you will have the opportunity to present your findings back to our wider research group in an internal meeting. If your contribution is included in a future publication, you will be acknowledged or if you meet the criteria for authorship, be named as a co-author. For exceptional candidates the work may provide a grounding for a future career in primary care research or a personal training award such as a pre-doctoral fellowship.

Entry requirements

You should have, or be studying, a relevant degree such as Medicine, Nursing, Paramedic Science, Social Sciences, including Sociology, Psychology, Communication Studies, Anthropology, or Humanities, such as Linguistics or English. An interest in qualitative research methods, health care communication and practical experience in Microsoft Excel and audio-editing software would be advantageous.

Physiology 01
Exploring the development and function of lymphatic vessels of the thymus

Primary supervisor

Dr David Grainger

Project description

The thymus is a primary lymphoid tissue responsible for the development of a diverse, yet self-tolerant, T-cell repertoire. This requires a constant ingress of immature progenitor cells and egress of fully functional T-cells into the periphery. This high level of trafficking is currently attributed to the extensive network of blood vessels that vascularise the thymus. However, in other organs this function is shared by the lymphatic vascular network. The presence of lymphatics within the thymus is disputed with contradictory reports published to date.

Using advanced imaging techniques, the project supervisors have identified the presence of lymphatic vessels within the aging thymus and are seeking a student to help them characterise these vessels for the first time. You will perform cutting edge tissue processing, staining and imaging techniques to help identify the ages, strains and potential functions of this vessel network. You will gain experience in developing interdisciplinary projects, have a good understanding of experimental design, frequently used methods, data analysis and interpretation. Discussion with members of the lab and wider institute would provide insight into the pros and cons of postgraduate study and what makes a successful PhD application. Ultimately, if of a high enough standard, data would be included in grant applications and/or a manuscript for publication.

Project outcome

You will gain experience in developing interdisciplinary projects, have a good understanding of experimental design, frequently used methods, data analysis and interpretation. You will learn how to perform dissection, sectioning and staining of adult organs to a high quality. Discussion with members of the lab and wider institute would provide insight into the pros and cons of postgraduate study and what makes a successful PhD application. Ultimately, if of a high enough standard, data would be included in grant applications and/or a manuscript for publication.

Entry requirements

You should have, or be studying, a Biological or Life Sciences degree and have an enthusiastic approach to learning about our field of study and experimental techniques.

Physiology 02
What is a memory made of?

Primary supervisor

Dr Anna Cook

Project description

Memory plays a crucial role in our lives. What we learn and remember shapes our behaviour, and memory loss can have severe consequences. Despite this importance, the molecular processes that underlie learning and memory are not completely understood. Our research group studies this using fruit flies, which can be trained to associate an odour with positive reinforcement like sugar, or negative reinforcement like a mild electric shock. These memories can persist for several days, leaving a physical memory trace in the brain.

This project will use advanced molecular and genetic techniques to identify some of the molecular pathways that are involved in making this memory trace. By knocking down candidate genes in particular neurons, then measuring the effect on memory performance, you will characterise mechanisms of memory formation and retrieval.

Project outcome

You will learn how to carry out behavioural assays to measure learning and memory performance. The assays used will depend on your interests. For example, you may learn how to set up a two-choice behavioural paradigm (T-maze) to measure learned associations between odours and positive or negative reinforcement. Or to study how memories are used to bias behaviour towards a goal, you will learn how to tether a fly to a treadmill and measure its behaviour as it navigates in an odour plume. You will use state of the art genetic and pharmacological manipulations to identify new molecular mechanisms of learning and memory. You will also have the opportunity to develop your presentation and science communication skills. If you are interested you will be able to watch and assist with other experiments taking place in the research group.

Entry requirements

You should have, or be studying, a degree in a subject related to Biology or Neuroscience, such as Psychology, Biochemistry, Zoology, Molecular Biology or a related discipline. Experience with lab techniques is helpful but training can be provided. The most important requirement is a willingness to learn and try new things, and troubleshoot (with help) when things don’t work as expected.

Physiology 03
Investigating mechanisms of heart and skeletal muscle regeneration in the Mexican Cavefish

Primary supervisor

Professor Mathilda Mommersteeg

Project description

The fish species Astyanax Mexicanus evolved in two distinct subpopulations, the river-dwelling surface fish and the cave-inhabiting cavefish due to different environmental pressures. This unique model system allows the study of molecular mechanisms of disease by performing intra-species comparisons. We have identified that surface fish can regenerate their hearts after injury while cavefish cannot. We are currently investigating if mechanisms implicated in successful cardiac regeneration are also involved in skeletal muscle repair.

We aim to study the differences in regeneration in both heart and skeletal muscle to understand the mechanisms instrumental to tissue recovery after injury and identify key pathways across tissues. The project will involve the bioinformatic interrogation of RNA sequencing datasets from Astyanax Mexicanus surface and cavefish heart and skeletal muscle to identify overarching pathways implicated in tissue regeneration. Additionally, the project will utilise confocal imaging of immunofluorescence and RNAscope staining of paraffin-embedded fish sections to examine identified pathways at a tissue level.

Project outcome

The project aims to provide the student with a rounded knowledge of heart and skeletal muscle biology. The project will introduce bioinformatic data analysis of RNA sequencing data and wet lab techniques as well as principles of good experimental design, data analysis and presentation during lab meetings. Data generated during the project may be potentially included in upcoming manuscript for future publication.

Entry requirements

You should have, or be studying, a degree in Biology, Life Sciences, or a related discipline. Basic knowledge of bioinformatics is not a requirement but would be advantageous You should be motivated and willing to learn new skills.

Population Health 01
AI for health of our future generations everywhere: Discovering sources of air pollution linked with global burden of Acute Respiratory Infection (ARI)

Primary supervisor

Professor Sara Khalid

Project description

This project explores how unregulated industrial emissions contribute to respiratory health risks in children, using real environmental and health data. You will use AI and satellite data to pinpoint pollution sources and see how they affect child respiratory health. You will analyse geospatial datasets of emission sources and health survey indicators to identify correlations between brick kiln density and Acute Respiratory Infections (ARI) in Asia. You will gain hands-on experience with data cleaning, visualization, and spatial analysis using our interactive web app (developed in collaboration with NDORMS, University of Oxford), which maps emission hotspots and child health outcomes.

For details, visit following websites:

1. University of Oxford: Pulse website
2. Cyclone Data Hub website
3. Neural Information Processing Systems website

Project outcome

You will gain hands-on experience working with real environmental and health datasets, learning how to process, visualize, and interpret geospatial data using platforms such as Google Earth Engine and Python. You will develop skills in linking environmental exposure data (industrial emissions, satellite imagery) with health outcomes (Acute Respiratory Infections in children) and in applying basic statistical and spatial analysis techniques.

By the end of the internship, you will produce a short data-driven report and visual summary identifying high-risk regions and their public health implications. You will also gain experience communicating scientific findings to non-technical audiences, and may be acknowledged in future publications or outreach materials based on this work.

Entry requirements

You should have, or be studying, a degree in Medicine, Public Health, Environmental Science, Data Science, Geography, or a related discipline. An interest in the intersection of health and environmental data is essential. Basic familiarity with data analysis (eg using Excel, R, or Python) and a willingness to learn simple statistical or geospatial techniques is desirable. No advanced coding experience is required, but curiosity about how data can inform public health and policy decisions will be important.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Population Health 02
Evaluating retinal language-image model (FLAIR) on UK Biobank colour fundus images

Primary supervisor

Professor Bartek Papiez

Project description

This project will evaluate the performance and interpretability of FLAIR, a large language–vision model, on colour fundus images from the UK Biobank. By comparing FLAIR’s disease predictions with existing clinical labels, the study aims to assess its potential for refining or supplementing sparse diagnostic codes for eye conditions such as glaucoma and diabetic retinopathy, as well as its zero-shot classification capabilities for systemic conditions like diabetes and hypertension.

The project may (subject to time constraints) also investigate whether FLAIR’s latent language representations can enhance clinical conditioning in generative vision architectures (eg diffusion models), enabling more morphologically and clinically accurate synthetic fundus images. Overall, this study will explore how multimodal AI can improve model control, clinical alignment, and the real-world utility of synthetic medical imaging data in research and diagnostics.

Project outcome

You will gain hands-on experience in AI model evaluation, medical image analysis, and the responsible application of advanced machine-learning methods to biomedical data. You will be trained in handling large-scale imaging datasets, curating and processing data, and benchmarking model performance using quantitative metrics. You will also learn how to interpret and visualise AI outputs in a clinical research context, contributing to open and reproducible practices in AI-driven healthcare research.

Entry requirements

You should have, or be studying, a degree in computer science, engineering, mathematics, physics, biomedical sciences, or a related quantitative discipline. Familiarity with Python programming and experience using common machine learning or data analysis libraries (eg PyTorch, NumPy, or Pandas) would be highly beneficial. An interest in artificial intelligence, computer vision, or biomedical data science is desirable, as is some prior exposure to image processing or statistical analysis. You should be motivated to learn, able to work independently, and willing to engage with interdisciplinary research at the intersection of AI and medicine.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

Population Health 03
Uncovering relationships between body composition and risk of disease

Primary supervisor

Dr Hannah Taylor

Project description

There are strong relationships between body fatness and the likelihood of developing cancer and/or cardiovascular disease (CVD) later in life. In this project you will look at the relationships between different measures of body fatness and the risks of developing different types of cancer and/or CVD, using data from UK Biobank participants. It will be possible to look at data from one time point, or to use data from different time points by making use of additional data collected at resurvey visits, to enable us to see how the risk of disease changes over time.

The differences in risk will be considered by using different statistical approaches and we will find out more about these associations by looking at the way in which factors such as age, sex, ethnicity and different blood-based biomarkers change the results.

Project outcome

You will have the opportunity to utilise large datasets and to uncover new insights into the causes of cardiovascular disease and/or cancer. By learning and employing statistical methods, you will be able to discover whether any important associations exist between different types of body fatness and composition and the development of disease later in life. Some of your findings may be relevant for a future scientific publication and you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a degree affiliated with Human Biology or Medical Sciences, with some skills in statistical analysis and scientific writing. Alternatively, applications are welcome from individuals who have, or are studying, a degree in Mathematics or Statistics, and have an interest in human health and disease.

Population Health 04
Plant-based diets and brain health

Primary supervisor

Dr Jessie Wang

Project description

Plant-based diets, including vegetarian and vegan diets, have become increasingly popular in recent decades. While there have been a number of studies investigating the associations between plant-based diets and various health outcomes, their relationship with brain function and health remains scarcely studied. Neuroimaging data include diverse measures of brain structure and function that have been associated with cognitive function and dementia risk, and are informative as early markers of neurodegenerative disease. The UK Biobank study includes a cohort of approximately 0.5 million British participants, among whom 1-2% overall (25% among British Indian participants) follow vegetarian or vegan diets. A subset of approximately 50,000 participants has available neuroimaging measures.

The aim of this project is to investigate associations between diet group of varying degrees of animal food exclusion and ethnicity with neuroimaging outcomes (eg total brain volume, total grey matter volume), using data from the UK Biobank cohort study.

Project outcome

You will learn epidemiological and statistical concepts and methods, including but not limited to, how to plan analyses, how to use and analyse large datasets with statistical software, and how to interpret and present epidemiological findings. Towards the end of the project, you may have the opportunity to contribute to a scientific publication and present your work at meetings.

Entry requirements

You should have, or be studying, a degree in Public Health, Nutritional Sciences, Biological Sciences, Neuroscience, Medicine, Statistics, Data Analysis, or another related discipline.

Population Health 05
Cancer epidemiology

Primary supervisor

Dr Christiana Kartsonaki

Project description

The aim of the project will be to study risk factors or biomarkers for certain types of cancer. The specific objectives can be adapted to match your interests and background. The project may involve a systematic review and meta-analysis, or other literature review and/or data analysis. For example, it may be a systematic review and meta-analysis on a particular risk factor and cancer type. Alternatively it could be on the analysis of a cancer-related dataset.

Project outcome

You will learn how to search the literature, use the statistical software R to analyse data, plan research and perhaps write a protocol or analysis plan, and some epidemiological and statistical concepts and methods.

You may have the opportunity to contribute to a paper to be submitted for publication.

Entry requirements

There are no specific degree requirements for this project. You should have an interest in epidemiology, medicine, health, (bio)statistics or another related field.

Population Health 06
Comparison of dietary motivations between omnivores, flexitarians, pescatarians, vegetarians and vegans in the Feeding the Future (FEED) study

Primary supervisor

Dr William Bell

Project description

Plant-based diets, which include little or no food from animal sources, have become increasingly popular in western countries in recent decades. Previous studies have examined the potential motivations for adopting plant-based diets, predominantly those excluding all animal foods (ie vegan diets), and found that concern for animal welfare was a leading reason. To date, few studies have examined motivations for different plant-based diets. Therefore, less is known about the motivations for adopting different plant-based diets and whether they differ across diet groups.

The aim of this project will be to examine motivations associated with following omnivorous, flexitarian (meat reducing), pescatarian (don’t eat meat but eat fish), vegetarians (no meat or fish), and vegan diets, and how they compare across diet groups in the Feeding the Future (FEED) Study, a study of ~6,000 adults following different plant-based diets. Associations with related socio-demographic and/or nutritional characteristics may also be explored.

Project outcome

As well as the opportunity to gain experience of, and skills in, epidemiological research, data analysis and nutrition research, you will present locally and produce a short research report, with the aim of submitting this for publication in a peer-reviewed medical journal.

Entry requirements

You should have, or be studying, a degree in Biomedical Sciences, Statistics, Public Health or a related discipline. You should have an interest in epidemiology, data analysis, medical research, psychology/behavioural research, or nutrition. Some experience or interest in statistics for data analysis would be beneficial but not essential, with relevant training provided.

Population Health 07
Ethnicity and breast cancer

Primary supervisor

Dr Toral Gathani

Project description

The project will aim to study an aspect of the associations of ethnicity with breast cancer. The project may involve a systematic review (or any other type of review) of the literature and/or data analysis. You will learn how to search the literature, use the statistical software R to analyse data, and some epidemiological and statistical concepts and methods.

Project outcome

You will be trained in searching the literature and analyse data using R. By the end of the project, you may have the opportunity to contribute to a manuscript for publication and give a presentation locally.

Entry requirements

You should have, or be studying, Medicine or a health-related degree, such as Public Health or Epidemiology.

Population Health 08
Are all ultra-processed foods the same? An analysis of the association between different types of ultra-processed foods and risk factors for cardiometabolic disease.

Primary supervisor

Professor Jennifer Carter

Project description

Ultra-processed foods (UPF), defined by the NOVA classification, are considered 'industrial formulations made mostly or entirely with substances extracted from foods …. with little if any whole food added'. (See the article 'Does the concept of “ultra-processed foods” help inform dietary guidelines, beyond conventional classification systems? YES', on the Science Direct website). High consumption of UPF has been associated with a range of chronic non-communicable diseases. The NOVA classification, however, places all UPF in the same group, even if there are differences in terms of the amount and types of substances present.

We have food consumption and health-related data from around 6700 American people from the National Health and Nutrition Examination Survey (NHANES) 2021-2023. Previous analyses have divided the consumption in UPF foods into separate dietary patterns based on the types of UPF foods consumed (such as a pattern of eating greater amounts of take-aways vs those mainly cooking with UPF ingredients), and these dietary patterns of UPF consumption have been associated with obesity. However, it is unclear if these different dietary patterns of UPF consumption relate to risk factors for cardiometabolic disease. This project will look at different dietary patterns of UPF consumption and assess how they relate to blood lipids, blood pressure and blood sugar.

Project outcome

You will be trained in quantitative statistical analysis and have experience with processing and analysing big data. You will be working in a department surrounded by Masters and DPhil students working on similar projects. At the end of the project you will present your findings back to the group in an internal meeting. If any aspect of your analysis is included in a future publication, you may be included as a named co-author on that paper. You will be encouraged to submit your analysis to a conference as a presentation.

Entry requirements

You should have, or be studying, a degree in Biological Science, Nutrition, or Medicine. No prior training in statistics is required, but you should be interested in learning quantitative skills.

Psychiatry 01
Genetics of Premenstrual Dysphoric Disorder

Primary supervisor

Dr Isabelle McGrath

Project description

Premenstrual dysphoric disorder (PMDD) is a psychiatric condition characterised by symptoms limited to the luteal phase of the menstrual cycle. PMDD is challenging to diagnose, has significant impact on quality of life, and the first line treatment of antidepressants are not effective for all. The biological underpinnings of PMDD remain largely unexplored.

This project will use genetic data from large population biobanks to investigate the biological basis of PMDD. Techniques such as genome-wide association study (GWAS), polygenic risk scores (PRS), and genetic correlation will be employed. The project aims to identify genetic variants and biological pathways associated with PMDD, and characterise its genetic relationship to other psychiatric conditions, improving our understanding of its mechanisms.

Project outcome

You will work alongside a postdoctoral researcher within a team focussed on psychiatric genomics. You will gain experience in analysing large biological datasets. At the end of the project you will present your findings back to the group in an internal meeting. If any aspect of your analysis is included in a future publication, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a degree in Biology, Biomedical Sciences, Medicine, Statistics, Public Health or a related discipline. You should have experience in R or python. This project will use quantitative data analysis, so experience of and/or an interest in statistics would be beneficial. If you have had no prior training in statistics, you will have the opportunity to learn these skills during the internship.

Psychiatry 02
How does the immune system contribute to brain health?

Primary supervisor

Professor Lahiru Handunnetthi

Project description

Inflammation is increasingly recognised as a key driver of neurodegenerative and psychiatric disorders. This project explores how human microglia, the brain’s resident immune cells, respond to disease-relevant inflammatory signals.

Using induced pluripotent stem cells (iPSCs), we will generate microglia-neuronal co-cultures and expose these to inflammatory stimuli linked to neurodegenerative and psychiatric disorders. We will subsequently assess changes microglia morphology and immune signalling in response to the disease relevant stimuli.

This project offers hands-on experience in stem cell biology, high content imaging, immune profiling and data analysis pipelines. This will provide a window into cutting-edge neuroscience and immunology research, and you will have the opportunity to work with an interdisciplinary team of clinicians and scientists.

Project outcome

You will gain hands-on experience in human stem cell culture, including the differentiation of induced pluripotent stem cells (iPSCs) into microglia and neurons. You will be trained in high-content imaging, immune profiling techniques, and data analysis pipelines.

Through this project, you will develop an understanding of how inflammation shapes cellular behaviour in neurodegenerative and psychiatric disorders. You will also gain exposure to interdisciplinary research, working alongside neuroscientists, immunologists, and clinicians.

At the end of the project, you will present your findings to the research group, and your work may contribute to publications.

Entry requirements

You should have, or be studying, a degree in Biomedical Sciences, Neuroscience, Biochemistry or a related discipline. Some prior laboratory experience, such as basic cell culture or molecular biology techniques, would be advantageous. An interest in neurobiology and immunology is desirable. You should have good organisational skills and enthusiasm for learning new experimental and analytical methods.

Psychiatry 03
Multi-omic analysis of circadian rhythm genetics using hourly blood samples

Primary supervisor

Dr Clara Albiñana

Project description

This project explores how our genes influence the body’s 24-hour biological clock, known as the circadian rhythm, which regulates sleep, metabolism, and immune function. Using a unique dataset of blood samples collected every hour over a full day, we will examine how gene activity (RNA) and protein levels change across time. Advanced computational and statistical techniques—including rhythmicity analysis and rhythmic quantitative trait locus (rQTL) mapping—will be used to identify genetic variants that affect the timing and strength of these daily molecular cycles.

By integrating transcriptomic and proteomic data, we aim to uncover how genetic differences shape the coordination between RNA and protein rhythms. This research will create the first genome-informed, multi-omic map of human circadian regulation, offering new insight into how disrupted rhythms contribute to conditions such as sleep disorders, diabetes, depression, and cardiovascular disease.

Project outcome

You will gain hands-on experience in computational biology and bioinformatics, learning to process and analyse large-scale multi-omic datasets, including RNA-sequencing and proteomic data. You will be trained in statistical and rhythmicity analysis using R, developing skills in identifying time-dependent molecular patterns and linking them to genetic variation. Through this, you will build an understanding of circadian biology, human genetics, and data integration techniques. You will also develop transferable skills in data visualization, scientific interpretation, and critical evaluation of complex datasets. During the project, you will work closely with experienced researchers, participate in group discussions, and contribute to interpreting biologically meaningful results. At the end of the placement, you will present your findings to the research team, and if your work forms part of a manuscript or conference presentation, you may be acknowledged or included as a co-author.

Entry requirements

You should have, or be studying, a degree in a relevant discipline such as Biological Sciences, Bioinformatics, Genetics, Data Science, or a related quantitative discipline. A solid understanding of molecular or cellular biology will be beneficial, along with an interest in genomics and circadian biology. You should have basic proficiency in data analysis and programming, ideally with experience in R or Python. Familiarity with statistics and concepts such as gene expression or omics data would be advantageous. The project suits students who are curious, detail-oriented, and comfortable working with large datasets. You should be able to work independently while engaging collaboratively with the research team. Prior experience with bioinformatics tools, data visualization, or computational modelling is helpful but not essential.

Psychology 01
An introduction to clinical psychology mental health research

Primary supervisor

Professor Daniel Freeman

Project description

You will learn about key aspects of the clinical research we do in our clinical psychology research group: the different types of clinical research (eg theoretical development, qualitative studies, experiments, clinical trials); research processes (especially clinical trials); working with people with lived experience in research; evidence-based clinical interventions; and the use of technology in providing psychological interventions.

We will help set up a research project with the aim of it later being submitted for publication, with the you as a co-author. The focus will be on analysing existing clinical data, and will provide experience of formulating a research idea, statistical analysis, and summarising findings. There will be supervision sessions for the project several times a week.

Project outcome

You will produce a technical report that may be submitted for publication.

Entry requirements

You should have, or be studying, a degree in Psychology. This project would suit someone who is interested in studying and/or treating mental health conditions.

Psychology 02
Generalised anxiety disorder in adolescents: A science communication project

Primary supervisor

Dr Polly Waite

Project description

Whilst some worry is typical for us all, some young people experience chronic and excessive worry about many different things – such as their health, relationships, finances, and work or study. This is type of clinical anxiety is called Generalised Anxiety Disorder (GAD). Whilst GAD has a large negative impact on adolescents’ lives, it is often wrongly overlooked as ‘just worry’ or as the least serious of the anxiety disorders.

Our work is aiming to change this by increasing understanding of what GAD looks and feels like for young people, and using this knowledge to develop more effective psychological therapies for those who need them in the future. So far, we’ve found that young people with GAD find uncertainty particularly challenging, and that they use unhelpful coping strategies such as seeking reassurance, overpreparing for situations, and avoiding things that worry them.

It's really important that these findings (and others) are shared with young people and professionals in a clear and meaningful way. We are therefore seeking an intern to help us with a number of science communication tasks – such as creating short videos for social media and generating resources for clinicians. To make sure that output is relevant for its target audience, you will incorporate feedback from young people with lived experience of generalised anxiety and professionals who work with them.

Project outcome

You will have the opportunity to develop skills in science communication, learn the importance of Patient and Public Involvement (PPI) in research, and gain insight into mental health research. Creative and writing skills are key to this internship, as it involves translating scientific findings into accessible and engaging content and messaging. At the end of the project, you will have the opportunity to present your work to our team and for it to be disseminated more widely.

Entry requirements

You should have, or be studying, a degree in Psychology or a related discipline, and have a particular interest in mental health and science communication. Some basic knowledge about mental health would be helpful, but extensive background knowledge is not required. We’re looking for someone who is open to expanding their skillset and trying new things.

Psychology 03
Measuring toddler response styles when tackling tasks that aim to elicit executive functions

Primary supervisor

Dr Alexandra Hendry

Project description

Executive function difficulties are linked to poor mental and physical health, poorer academic outcomes, and lower quality of life, across a range of populations (Diamond, 2013; Snyder, Miyake and Hankin, 2015; Wallace et al., 2016). In this project you will work with video and performance score data collected from 200 toddlers with and without a family history of autism and ADHD to consider how response styles interact with cognitive and regulatory skills to shape behaviour. By reconceptualising how to measure executive functions in toddlerhood we may be able to better chart the development of these important skills. This project, as with all of the team's research, takes a neurodiversity-affirming approach.

Project outcome

You will be trained in behavioural coding, and basic statistical analysis. At the end of the project you will present your findings back to the group in an internal meeting. If any aspect of your analysis is included in a future publication, you may be included as a named co-author on that paper. You will have the opportunity to join lab meetings involving researchers engaged in both basic and applied science. In these meetings (and in additional one-to-one sessions if requested) we will discuss the 'hidden curriculum' of higher education and research, and explore ways to flourish in academia.

Entry requirements

You should have, or be studying, a Psychology related degree and have standard software development skills at an undergraduate level. Familiarity with R would be useful, but is not essential.

Psychology 04
Understanding the human visual system through high-resolution retinal imaging

Primary supervisor

Professor Hannah Smithson

Project description

The human retina captures light and converts it into electrical signals in the human central nervous system (CNS). It is a directly observable part of the CNS, providing a unique 'window' to the brain.

Research in the Oxford Perception Lab uses an adaptive optics scanning laser ophthalmoscope (AO-SLO) and an adaptive optics optical coherence tomography (AO-OCT) system developed in the lab to image the living human retina at single-cell resolution. We also develop computational techniques to better analyse and interpret the data or to theoretically understand the early visual system and neural processing in the retina.

This project will be supervised by Prof Hannah Smithson, Dr Jiahe Cui and Ms Sirui Liu. It will utilise advanced retinal imaging or computational techniques to deepen our understanding of the human visual system. Specific project aims will be defined before the start of the internship, including but not limited to, data collection (including human participant imaging), data analysis, and software development.

Project outcome

You will gain practical experience in cutting-edge retinal imaging technology, human psychophysical studies, data and image analysis algorithms, theoretical modelling techniques or machine learning. You will be immersed in an interdisciplinary environment working with both engineers and vision scientists and have opportunities to learn about a range of different research projects in the field of vision science.

You will join weekly lab meetings and be given the opportunity to present your own work at the end of the project. If an aspect of your work is included in a future publication, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying a science degree. Our research is interdisciplinary, and we welcome students from a range of backgrounds, including Experimental Psychology, Neuroscience, Engineering, Physics, or Computer Science, with an interest in addressing visual neuroscience questions. A degree of familiarity with Python or Matlab programming would be useful.

Anthropology 01
Co-parenting among the chaos: what we can learn from parents with toddlers during the UK lockdown

Primary supervisor

Dr Paula Sheppard

Project description

Biparental care is a defining feature of our species; women are able to produce and raise multiple dependent children because they have help from others: partners, grandparents, even friends. However, we also have strong cultures around the division of labour: in contemporary capitalist economies, at least one, but often both, parents work outside of the home and domestic labour (household chores), including childcare, has to be negotiated between them. In the UK, one of the reasons that people have very few children nowadays is because co-parenting duties tend to fall more heavily on the mother and this makes parenting and work-life more incompatible for women. This gender inequality in the family can lead to resentment and ultimately to avoiding having more children.

This study will use interview data collected during the UK lockdown (2020) from mothers and fathers of toddlers to explore how co-parenting practices were affected during this time, and what this may teach us about parenting challenges today. The data will be thematically analysed and written up into an article for publication.

Project outcome

You will be trained in qualitative methods such as thematic and content analysis and will learn to use NVivo software. You will also gain experience working within a research team which requires good communication skills, responsibility, and time-keeping. You will be co-authors on the main output; an original research paper to be published in a peer-reviewed social science journal, which will be an excellent addition to the CV of an aspirant masters or doctoral student.

Entry requirements

There are no specific degree requirements for this project. You should have an interest in evolutionary anthropology, sociology, qualitative demography, or a similar social science discipline. Good attention to detail and an ability to learn new skills independently are essential for this project.

Biology 05
Seeing nature through citizen eyes: analysing flower colour and spread in an invasive species

Primary supervisor

Professor Rob Salguero-Gomez

Project description

This project will explore patterns of flower colour and distribution in an invasive plant species using photographs submitted by the public through citizen science platforms. You will collect, organise, and analyse image data to investigate how flower colour varies across regions and whether environmental factors influence these patterns. You will gain hands-on experience in data management, programming, and the use of accessible artificial intelligence tools to help automate image labelling and analysis. The project also involves spatial data exploration to understand distribution trends. Through this work, you will develop transferable coding and research skills, deepen your understanding of invasive species ecology, and appreciate the value of citizen science in advancing environmental research and engaging the public in scientific discovery.

Project outcome

You will gain experience in handling and analysing large citizen science image datasets, learning how to extract meaningful information about flower colour and distribution patterns. You will be trained in programming (using Python or R), data cleaning, image classification, and introductory machine learning methods to support automated labelling. You will also learn principles of programming reproducibility and how to create and maintain your own coding portfolio using GitHub.

By the end of the project, you will have produced a short written report and co-created a video explaining your findings. You will also present your results to the research group and may be acknowledged in future research outputs arising from the project.

Entry requirements

You should have, or be studying, a degree in Biology, Environmental Science, Geography, or a related quantitative subject such as Data Science or Computer Science. Some experience with data handling, programming (preferably in Python or R), and an interest in applying computational tools to ecological questions would be advantageous. Familiarity with image analysis, machine learning, or GIS software is desirable but not essential, as training will be provided. You should be curious, motivated to learn new analytical techniques, and comfortable working independently with guidance. An enthusiasm for biodiversity, citizen science, and communicating research to the public will help you get the most from this project.

Clinical Medicine 01
International health workforce recruitment in the UK

Primary supervisor

Dr Yingxi Zhao

Project description

The UK’s health and care services face major challenges in recruiting and retaining sufficient staff. Currently, one in five NHS or social care workers in England is internationally recruited, a figure that is expected to rise. Private recruitment agencies are playing an increasingly central role in facilitating the international migration of health workers, particularly nurses, against the backdrop of a global workforce shortage. This internship, building on work by the Health Systems Collaborative team on UK and global health workforce issues, will involve a series of literature, policy reviews, and web-based content analysis to map recent policy changes, identify key actors, and update the evidence base on the experiences of internationally recruited staff.

Project outcome

Through this project, you will develop skills in conducting comprehensive literature and policy reviews, learn how to design search strategies and apply thematic coding, and gain experience of working with an applied health research team and applying analytical frameworks and theories.

We will expect you to produce a report or other relevant output to disseminate the findings - and to present this to colleagues in the Health Systems Collaborative team. If any aspect of your analysis is included in a future publication, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a degree in Medicine, Allied Health, or Social Sciences (eg Education, Policy, Sociology, or Anthropology). Applicants from other disciplines are also welcome to apply, provided they can demonstrate a strong interest in social science or health services research, along with relevant skills such as literature and systematic reviews, and clear written and verbal communication.

Engineering 11
Digital equity in disaster scenarios

Primary supervisor

Professor Noa Zilberman

Project description

The world is threatened by disasters ranging from severe weather events to pandemics. While technologies like remote healthcare and online education offer critical support, not everyone has equal access to them. This digital divide creates significant inequality during crises, as many cannot use these tools even when available.

As part of this project, you will develop hardware or software mechanisms to improve digital equity for UK residents during disasters. The goal is to enhance accessibility to key digital services. Solutions might include autonomous remote healthcare monitoring, alerting systems, or low-cost, low-latency AI services for the home.

This project is interdisciplinary and covers topics in computer architecture, computer networks, software engineering, and digital equity. AI is an optional, relevant field of study for this work.

Project outcome

As part of this project you will choose to focus on hardware, software or hardware/software co-design. You will explore advanced computing technologies, efficient coding practices, digital equity and potentially AI. You will gain experience in advanced data processing, scripting, power/performance-aware programming, software/hardware development, publishing code and artefacts, and you will help improve digital equity in the UK and our preparedness to future crises. If any publication opportunities will arise from the project, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a degree in Computer Engineering, Electrical Engineering, or Computer Science. Excellent programming skills in Python or C/C++ or Verilog and basic knowledge in computer architecture or computer networks are required.

Engineering 12
Artificial intelligence for cancer detection in medical imaging

Primary supervisor

Professor Jens Rittscher

Project description

This project will investigate how artificial intelligence can be used to support cancer detection in medical images, for example endoscopic images and videos and other publicly available imaging datasets. The focus will be on designing and evaluating deep learning methods for tasks such as lesion segmentation and detection in realistic data.

You will work with de-identified and public datasets, curate suitable training and evaluation subsets, and implement model training and evaluation pipelines in Python and PyTorch. Supervision will be provided through regular meetings, discussion of experimental plans, and feedback on results, but the work will not follow a step-by-step tutorial format. Instead, you will be encouraged to take intellectual ownership of a well-defined research question and to explore model design choices and performance trade-offs in a systematic manner.

Project outcome

You will gain experience working with medical imaging data (for example, endoscopy images and videos) and additional public datasets, including basic pre-processing and organisation. You will have the opportunity to develop practical skills in data curation, including constructing, cleaning, and documenting training, validation, and test sets. You will implement and train deep learning models in Python and PyTorch for segmentation and/or detection, and evaluate them using appropriate quantitative metrics. You will learn how to design and run controlled experiments (eg comparing architectures, hyperparameters, or data splits) and interpret the results in a clinically relevant context and you will develop scientific communication skills through written documentation and an end-of-project presentation to the research group.

Entry requirements

You should have prior experience in Python and implementing and training deep learning models using PyTorch. You should also have familiarity with basic machine learning concepts, such as, training, validation, test splits, loss functions, optimisation, overfitting, and/or evaluation metrics). Experience with image data and computer vision would be advantageous. No prior medical or biological training is required.

Funding information

This project may be funded by the Google DeepMind Research Ready Scheme.

English 01
Knotted Histories: Early modern global carpets, global exchange and the public country house

Primary supervisor

Professor Nandini Das

Project description

This project will involve working with the team on the 'Knotted Histories: Early Modern Global Carpets, Global Exchange and the Public Country House’ project, run between the Faculty of English at Oxford and the National Trust. This project aims to reconsider cultural and global contexts of early modern carpet production and their use beyond traditional approaches, focusing on craft makers and manufacturers alongside an examination of the cultural meaning of carpets.

As part of this, the project is focusing on a series of National Trust properties as Case Studies, considering both their historic and current carpet collections and the role carpets played in social spaces within these properties. This study is conducted through archival analysis, examining catalogues, wills, inventories, and other documentation as appropriate, as well as through material analysis, considering the physical qualities and designs of the carpets.

Project outcome

The main task on this project will be producing a set of Case Studies for a selection of National Trust properties, modelled on the project’s existing Case Studies. Each of these Case Studies will include a historical overview of the property’s history, details on its existing carpet collections, analysis of its previous holdings, and material analysis of the carpets where possible. These Case Studies will also contain lists of possible archival sources identifying potential leads relevant for future work on the project.

Through this internship, you will have an opportunity to work with an interdisciplinary team engaged in work on material history, public engagement, early modern global networks, and wider questions of living and lived heritage. You will also gain experience working with heritage collections and considering some of the challenges facing heritage institutions. You will also gain experience working with special collections and manuscript materials, an insight into research libraries and archives, and experience working with humanities data.

At the end of the project, you will present your research to the project team in an internal meeting, and you will be credited if your material is used in future work. Supervisors will also assist you to write up any material you are particularly interested in for a post on the TIDE @ Oxford blog to be published at the end of the internship.

Entry requirements

There are no specific degree requirements for this project. You should be able to work independently, have basic IT skills and familiarity with Excel. You should have in interest in early modern history, art or literature, material history and working with heritage institutions. Experience in early modern palaeography, in particular secretary hand, would be advantageous but not essential as training can be provided.

Geography 01
Measuring recovery of post-agricultural temperate forests

Primary supervisor

Professor David Moreno Mateos

Project description

We aim to understand how forests recover their complexity after the end of agriculture. This is a rapidly growing trends globally that will only increase in the coming decades as agriculture intensifies.

You will be involved in soil and plant sample collection in the field and in processing those and other samples in the lab for their analysis. This includes soil samples preparation from chemical analysis or DNA extraction and purification for genomic analysis. Understanding the recovery process will help us design more efficient tools to recovery the complexity of restored forests.

Project outcome

You will learn to design field sample collections in response to specific research questions. You will work with those samples in a lab and prepare them for external analysis in specialized facilities. This will help you to have a first interaction with molecular biology techniques and how they can be used to improve current conservation and restoration efforts.

You will have the opportunity to engage with several research groups in the School of Geography and the Environment and potentially the Department of Biology in talks, lab meetings and discussion groups with other undergraduate students, master’s students, DPhil students, postdoctoral researchers and faculty staff. You will have a chance to discuss the potential statistical approaches to use with the data you have generated and will have the opportunity of participating in publications derived from this research.

Entry requirements

You should have, or be studying, a degree in Environmental Science or equivalent (such as Biology or Physical Geography). Previous experience in molecular biology techniques would be advantageous.

Geography 02
Cultures of care, choice, and challenge: Exploring student and early career researcher expectations and experiences of animal use in science

Primary supervisor

Professor Beth Greenhough

Project description

A recent report by the RSPCA's Animals in Science Department (authored by those on the supervisory team) found that some UK students and early career researchers (ECRs) working with animal models experience limited discussion around alternatives to animal use and insufficient support for the emotional and ethical challenges that animal use can raise.

Informing realistic expectations for projects involving animal use, enabling flexibility around model choice, and providing a supportive environment are crucial for empowering emerging researchers to adopt non-animal methods (NAMs) and constructively engage with the challenges of animal use.

This qualitative social science project aims to develop further insights into how students and ECRs 'come into' animal work, their expectations, sense of choice and agency, and experiences of support. To do so, it will involve an interview study with ~10 students and ECRs who are currently, or have recently, engaged in research involving the use of animals.

Project outcome

You will gain experience and skills in reviewing academic literature, conducting in-depth semi-structured interviews, transcription, and undertaking thematic analysis. As the project is co-supervised by the RSPCA's Animals in Science Department, this project will provide an opportunity to work with the world's oldest animal welfare charity and gain experience of communicating research findings across sectors. If two students across the social sciences and biosciences are hosted, then you will also benefit from cross-disciplinary working. At the end of the project, you will have an opportunity to present your analysis to the research team and co-author a report and/or blog post, adding to the literature base on this important topic.

Entry requirements

You should have, or be studying, a social science-related degree, such as Sociology, Anthropology, or Human Geography, or a bioscience-related degree with an interest in the social and ethical aspects of scientific practice. You will be open-minded and able to work sensitively on complex and controversial topics. Understanding and experience of qualitative social scientific research is desirable but not essential.

Geography 03
Exploring the land surface response to global warming in high resolution convection permitting models using simplified theoretical models.

Primary supervisor

Dr Jerry B. Samuel

Project description

Soil moisture is an important parameter in land-atmosphere interactions. However, complex land surface models have challenges in accurately simulating soil moisture and the associated water and energy fluxes across the land surface. This contributes to uncertainties in projected changes in land-atmosphere interactions under global warming.

High resolution convection permitting models, capable of resolving processes that were hitherto parametrized, are expected to produce much better simulations of the climate system. These models predict delayed onset of monsoon rains, more dry days, and possibly more intense droughts, despite larger seasonal mean rainfall in various monsoonal regions under global warming.

This project will use a simplified energy balance approach to identify prominent processes that influence changing land surface characteristics during the monsoon season in these state-of-the-art simulations, with an aim to advance the understanding of the major drivers of future droughts.

Project outcome

You will join the Climate Research Lab to work closely with a team analysing climate observations and models and will:

• gain data analysis skills in Python;
• gain knowledge of the latest cutting-edge atmospheric models;
• gain skills working as part of a research team; and
• gain scientific communication and presentation skills.

It is possible that your contributions lead to a short paper/conference presentation communicating the outcome of your internship research.

Entry requirements

You should have, or be studying, a degree in a relevant field, such as Physics, Earth Science, or Geography, although we would welcome students in Mathematics or Computer Science with an interest in weather and climate. You should have basic familiarity with software coding, such as Python or similar. You should be interested in atmospheric and climate science, but in-depth prior knowledge is not required.

Geography 04
Exploring observations and simulations of high-impact storms in Africa

Primary supervisor

Dr Francesca Morris

Project description

Mesoscale convective systems (MCSs) are thunderstorms which can last for hours or even days, and span hundreds of kilometres. Not only are they high-impact weather systems which contribute extreme winds and rainfall, but they can also modulate atmospheric circulations at scales beyond their own structures.

Few observations of MCSs in Africa exist, but the Oxford Climate Lab have recently led several field campaigns in Southern and Eastern Africa, during which MCSs passed through the field sites. Observations were regularly taken using radiosondes and lidar, and could provide a unique insight into the winds associated MCSs, and therefore their upscale effects on the atmosphere. This project will explore these observations and assess their potential to contribute to our understanding of the winds associated with MCSs, comparing these to state-of-the-art ensemble weather forecasts from the UK Met Office.

Project outcome

You will join the School of Geography and the Environment’s Climate Lab, a team of researchers studying weather and climate and specialising in the Southern Hemisphere. By the end of the project, you will have:

1. learned about global and regional meteorology and climate science;
2. learned how to analyse in-situ measurements from meteorological field campaigns;
3. gained understanding of numerical weather prediction models and their output;
4. gained advanced data analysis skills in Python, particularly for analysing geospatial data;
5. gained practical knowledge of applying advanced statistical techniques;
6. gained scientific communication and presentation skills; and
7. contributed to a short paper communicating the outcome of your internship research.

Entry requirements

You should have, or be studying for, a degree in a relevant discipline, such as Physics, Earth Science, or Geography. We would also welcome applicants with a background in Mathematics or Computer Science with an interest in weather and climate. Familiarity with basic programming in Python (or a similar language) and the ability to use a basic command line interface for computing is highly desirable, however, training in basic computing will be provided. An interest in atmospheric and climate science is helpful, but in-depth prior knowledge is not required.