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# Researcher in AI-Driven Functional Genomics and Population Health

- **Company:** University of Oxford
- **Location:** Oxford, UK
- **Salary:** £39,424.0 - £47,779.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Big Data, Computational Biology, Python (Programming Language), Machine Learning
- **Published:** August 12, 2026
- **Apply:** https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form?p_company=10&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=188147

## About the Role

Applicants should hold, or be close to completing, a PhD/DPhil in bioinformatics, computational biology, statistical genetics, or a related field, with strong quantitative and computational skills. Experience applying AI or machine learning to large-scale biomedical datasets, along with strong programming ability in Python and/or R, is essential, as are excellent communication and collaborative skills.

## Description

Oxford Population Health (the Nuffield Department of Population Health) provides an excellent environment for multi-disciplinary research and teaching and for professional and support staff. We work together to answer some of the most important questions about the causes, prevention and treatment of disease.
We are seeking a Researcher to join a multidisciplinary team developing AI-driven genotype-to-phenotype models for human health at the Big Data Institute (BDI), University of Oxford. The successful candidate will contribute to a strategic research programme integrating population-scale biomedical data, multi-omics technologies, and artificial intelligence to generate interpretable biological insights.
Reporting to the BDI Director, the post holder will work within Oxford Population Health and the Big Data Institute, at the intersection of computational genomics, statistical modelling, and AI. The programme leverages large-scale resources, including UK Biobank and other international cohort studies, to develop biologically grounded AI systems linking genetic variation to complex phenotypes.
The post holder will lead a defined methodological workstream, contributing computational innovation and reproducible infrastructure while collaborating closely with cross-disciplinary teams across genomics, epidemiology, machine learning, and biomedical science. The role will also involve mentoring junior researchers within the group.