Methods to better map and understand the genetic contributors to human disease

We invite applications for a DPhil studentship to join Duncan Palmer and Pier Palamara’s research teams at the University of Oxford. The position is funded by the Pioneer Centre for Statistical and Computational Methods for Advanced Research to Transform Biomedicine (SMARTbiomed: https://smartbiomed.dk), a new international collaboration combining large-scale, multimodal biomedical data with advances in statistical and machine learning methods to improve the understanding, treatment and prevention of human disease. The successful applicant will develop and apply novel statistical and machine learning algorithms to address key challenges in human genomics, applying them to massive genomic datasets from across the globe.

Huge amounts of genetic and trait information (e.g. disease diagnoses, measurements, diet) are being generated for hundreds of thousands of individuals across the globe. Many robust associations between our genetics and these traits are being generated, but it is usually extremely difficult to zero in on their causal underpinnings, due to the correlated nature of our shared ancestry. Furthermore, traits are usually analysed in isolation, rather than sharing information between them, leaving statistical power on the table. Finally, machine learning methods have allowed us to better prioritise how likely a genetic variant is to cause disease, but this information is often not used in large scale genetic analysis. This project will seek to refine genetic association signals and advance our understanding of disease by sharing information across traits and guiding inference with prior knowledge, while maintaining computational feasibility.

Applicants should hold or be near completion of an undergraduate in a quantitative discipline such as mathematics, computer science, statistics, machine learning, statistical or population genetics, or a related field. Previous experience in statistical and/or population genetics or related fields is desirable but not essential, as we expect that the candidate will develop skills in working with genomics and biomedical data through the course of the project. This project will suit a quantitative researcher looking to build computationally efficient statistical and machine learning models for large-scale datasets.

The supervisors are Dr Duncan Palmer (Department of Statistics, University of Oxford) and Professor Pier Palamara (Department of Statistics, University of Oxford). Furthermore, the student will establish close ties with Professor Bjarni Vilhjálmsson (Department of Molecular Biology and Genetics, Aarhus University) and Professor Palle Duun Rohde (Department of Clinical Genetics, Aalborg University), including travel to Denmark for the collaboration.

The start date for the DPhil studentship is 06 October 2025. The DPhil studentship will be based in the Big Data Institute and Department of Statistics at the University of Oxford. This studentship will be for a maximum of 3.5 years duration; it includes fees at the UK/home rate, stipend, and research-related travel. 

The successful candidate will benefit from joining the Pioneer Centre for Statistical and Computational Methods for Advanced Research to Transform Biomedicine (SMARTbiomed: https://smartbiomed.dk, Professor Naomi Wray), funded by a consortium of Danish foundations supporting research collaboration between researchers at Danish universities/institutes and the University of Oxford and anchored at the National Centre for Register Research (NCRR) at Aarhus University. SMARTbiomed will support a critical mass of researchers who focus on method and software development for analysis of, and inference from, human health-related massive data, advancing applications in medicine. We aim to create a vibrant international community of researchers, both virtually and in-person, providing an exciting environment of collaboration to attract early-career researchers from a wide variety of fields, working as a team towards unified goals. 

Entry requirements and other useful information can be found at: DPhil in Statistics course information. 

Applications should be made online to the Department of Statistics via the Graduate Application Form and should include a CV, research proposal, three references, and a transcript of previous degrees. In the section of the application form “Departmental Studentship Applications” applicants will be asked whether they are applying for an advertised studentship. In this section please state “Yes” followed by “25STAT02DEP”.

Completed applications must arrive by midday on Monday 30 June 2025. Please quote “25STAT02DEP” in your research proposal.