D.Phil./Ph.D. Opportunities for entry in October 2027

Applications are open for entry to D.Phil. programmes at the University of Oxford. This year I am advertising projects in:

• Agentic AI for discovering epidemiological risk factors
• Agentic AI for antimicrobial resistance gene discovery and phenotype prediction
• Analysing large-scale electronic health records to identify at-risk populations

Prospective students wishing to propose their own project ideas in the area of infectious disease genomics are also encouraged to apply.

Agentic AI, in which the power of AI chatbots is harnessed to write code, run applications and mange projects, is transforming the practice of data science. Future students graduating from the group will have a command of the technology and know how to use it to make important biological discoveries into the genes and other factors that drive infections.

To join the group on a fully-funded D.Phil. studentship you will need to apply to one of the following programmes in which I can supervise your thesis as primary supervisor:

• D.Phil. in Population Health
• D.Phil. in Genomic Medicine and Statistics
• D.Phil. in Interdisciplinary Life and Environmental Science
• EPSRC Centre for Doctoral Training in Healthcare Data Science

I can also co-supervise students applying to other programmes at the University of Oxford, through my collaborators including Professor Sarah Walker and Professor David Eyre:

• D.Phil. in Clinical Medicine

All programmes are highly competitive and successful applicants will typically have a very strong track record of academic achievement and extracurricular experience in their chosen subject.

Past students

Check out our lab page to see current and former students including data scientists Nicolas Arning (D.Phil. in Interdisciplinary Bioscience; BioAI and formerly of Bayer), Steven Lin (D.Phil. in Genomic Medicine and Statistics; Co-Founder and CTO at Finaldose) and Jeffrey Chen (D.Phil. in Continuing Education; Head of AI at Combient), and clinical academic Bernadette Young (D.Phil. in Clinical Medicine; Fellow at Oriel College and Infection Consultant).

How to apply

Please follow the programme-specific links below. Every programme is highly competitive; students committed to pursuing a D.Phil. at Oxford are encouraged to apply to all programmes relevant to their expertise, area of interest and mode of study (part-time or full-time).

University programme	Department website	Deadline
D.Phil. In Population Health	Oxford Population Health	01/12/2026
D.Phil. in Genomic Medicine and Statistics	Centre for Human Genetics	01/12/2026
D.Phil. in Interdisciplinary Life and Environmental Science	ILESLA	06/01/2027
EPSRC Centre for Doctoral Training in Healthcare Data Science	Big Data Institute	01/12/2026
D.Phil in Clinical Medicine	Nuffield Department of Medicine	01/12/2026

Colleges

When applying you may be asked to choose a college. Most undergraduate colleges offer a more traditional experience, with the larger ones often offering on-site accommodation, while graduate colleges tend to be more modern and egalitarian. I would recommend St. John's College and St. Cross College, where I'm a member (though this doesn't affect the admission process).

New paper: Identifying direct risk factors for COVID-19 hospitalization in UK Biobank using Doublethink

This week sees publication of our new paper Identifying direct risk factors in UK Biobank via simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink in Proceedings of the National Academy of Sciences (PNAS). This work was joint with Nicolas Arning and Helen Fryer.

In this study we applied a novel approach called Doublehthink to implement an exposome-wide association study (ExWAS) of non-genetic risk factors that influence risk of COVID-19 hospitalization in UK Biobank (UKB). Inspired by Bayesian model averaging, our approach enhances power by testing both individual variables and arbitrary groups of variables.

We employed Doublethink to reveal exposome-wide significant signals across nine individual variables and seven groups of variables, notably factors like aging, dementia and prior infection overlooked by 85% of previous studies of the UK Biobank. We found significant direct effects among some commonly reported risk factors like age, sex, and obesity, but not others like cardiovascular disease. The effects of hypertension, depression, and diabetes appeared to be mediated via general comorbidity. 

Biobank-scale epidemiology has transformed the study of common diseases, particularly through the discovery of genetic risk factors using genome-wide association studies (GWAS) methods. ExWAS applies similiar logic to pursue agnostic, data-driven discovery of non-genetic risk factors.

Doublethink offers an ExWAS method that can test thousands of variables in hundreds of thousands of UK Biobank participants. It controls both Bayesian (false discovery rate; FDR) and classical (familywise error rate; FWER) measures of false positives. Its Bayesian model-averaging approach enables an agnostic approach to variable selection, but it also addresses drawbacks of Bayesian methods like computational burden and reliance on prior assumptions for its false positive control.

Through its capacity to interpret biobank-scale data, our new Doublethink-based ExWAS approach paves the way for future systematic analyses of risk factors for infectious and chronic diseases in UK Biobank and beyond.