Apply genetic epidemiology and machine learning as a full time, on-site 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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