Apply genetic epidemiology and machine learning as a full-time, in office Research Fellow at the University of Southampton. Study type 2 diabetes mechanisms using large-scale data.

Based at University Hospital Southampton, this mid-level fixed-term fellowship supports Professor Hanieh Yaghootkar’s precision-diabetes research programme. You will integrate organ-health measures with genetic and clinical data, working across genetic epidemiology, clinical medicine, and data science. The appointment runs until 31 January 2030.

Key Responsibilities:

- Identify diabetes subtypes using organ-health measures and machine learning
- Characterise clinical, metabolic, and genetic subtype profiles
- Conduct GWAS and Mendelian randomisation analyses




- Build models predicting diabetes risk and progression
- Analyse UK Biobank using reproducible computational workflows

Requirements:

- PhD in genetic epidemiology or a related discipline
- Large-scale genetic, epidemiological, or biomedical dataset experience
- Proficiency in R and/or Python and Linux environments
- Practical GWAS and Mendelian randomisation experience
- Peer-reviewed research outputs and strong communication skills

Develop precision-diabetes research through genetic epidemiology, machine learning, GWAS, and reproducible analysis at Southampton.

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