Research Fellow in Genetic Epidemiology and Data Science

The Faculty of Medicine at the University of Southampton invites applications for a three-year, full-time, fixed-term Research Fellow to join Professor Hanieh Yaghootkar's interdisciplinary precision-diabetes research programme. Based at University Hospital Southampton, you will investigate the biological mechanisms underlying type 2 diabetes by integrating measures of organ health with large-scale genetic and clinical data. Working alongside genetic epidemiologists, clinicians and data scientists, you will apply advanced statistical and machine learning approaches to identify distinct diabetes subtypes, investigate their genetic determinants and improve prediction of disease risk and progression.

As a Research Fellow, you will:

- Develop and apply machine learning approaches to identify distinct subtypes of type 2 diabetes using measures of liver, pancreas, adipose tissue and skeletal muscle health.
- Characterise the clinical, metabolic and genetic profiles of diabetes subtypes and evaluate their reproducibility across populations.
- Conduct genome-wide association studies (GWAS) of organ-health traits and diabetes subtypes, and apply Mendelian randomisation to investigate potential causal relationships.
- Develop and evaluate statistical and machine learning models to predict incident type 2 diabetes and disease progression using genetic and clinical data.
- Analyse large-scale population datasets, including UK Biobank, using robust statistical methods and reproducible computational workflows.
- Contribute to high-quality scientific publications, conference presentations and the dissemination of research findings.
- Collaborate with multidisciplinary researchers across genetic epidemiology, genomics, clinical medicine, imaging, statistics and data science.

You will have opportunities to develop your research expertise, contribute to innovative analytical approaches and build your publication record within an internationally collaborative research environment.

About you:

- A PhD in genetic epidemiology, statistical genetics, biostatistics, data science, computational biology or a closely related discipline.
- Experience analysing large-scale genetic, epidemiological or biomedical datasets, ideally including UK Biobank.
- Strong statistical and computational skills, including proficiency in R and/or Python and experience working in Linux-based computing environments.
- Practical experience conducting GWAS and Mendelian randomisation analyses, including appropriate quality control, sensitivity analyses and interpretation.
- Experience applying machine learning, clustering, predictive modelling or other advanced statistical methods to complex biomedical data.
- An understanding of genetic epidemiology and the application of statistical methods to investigate complex disease mechanisms.
- Evidence of high-quality research outputs, including peer-reviewed publications.
- Excellent analytical, organisational and communication skills, with the ability to work independently and collaboratively within multidisciplinary research teams.
- Experience with metabolic disease research would be advantageous.

What we can offer:

- Access to large-scale biomedical datasets, high-performance computing infrastructure and secure analytical environments.
- Opportunities to work at the interface of human genetics, medical imaging, machine learning and metabolic disease research.
- Collaboration with leading researchers across clinical medicine, genomics, epidemiology and computational science.
- Opportunities for high-impact publications, scientific conferences, professional development and career progression.
- A supportive and inclusive working environment committed to equality, diversity and flexible working, including Athena SWAN principles.
- A competitive University benefits package, including pension provision and generous annual leave.

A standard DBS check is required for this role.