Research Fellow in Genetic Epidemiology and Data Science

Unist; Southampton; On-site; GBP 42,000 - 54,000; Full time

Benefits offered by this job

Athena SWAN principles

Flexible working

Pension provision

Generous annual leave

Job summary

The Faculty of Medicine at the University of Southampton invites applications for a three-year, full-time Research Fellow to join Professor Hanieh Yaghootkar’s interdisciplinary precision-diabetes programme at University Hospital Southampton.

You will apply statistical and machine learning methods to identify diabetes subtypes, analyse GWAS, and build models to predict disease risk and progression using UK Biobank and other datasets.

Qualifications

• PhD in genetic epidemiology, statistical genetics, biostatistics, data science, computational biology or closely related discipline.
• Experience analysing large-scale genetic, epidemiological or biomedical datasets (UK Biobank preferred).
• Strong statistical and computational skills; proficiency in R and/or Python; Linux environments.
• Experience with GWAS and Mendelian randomisation analyses, including QC and sensitivity analyses.
• Experience applying ML or advanced statistical methods to biomedical data.
• Demonstrated high-quality research outputs (publications).

Responsibilities

• Develop and apply machine learning to identify diabetes subtypes from organ-health and genetic data.
• Characterise clinical, metabolic and genetic profiles of diabetes subtypes across populations.
• Conduct GWAS of organ-health traits and subtypes; apply Mendelian randomisation.
• Develop models to predict incident diabetes and progression using genetic and clinical data.
• Analyse large-scale datasets (e.g., UK Biobank) with robust workflows.
• Contribute to publications and conference presentations.
• Collaborate across multidisciplinary research teams.

Skills

R and Python

GWAS

Mendelian randomisation

Large-scale data

Linux computing

Statistical modelling

Communication skills

Education

PhD in genetic epidemiology / related

Tools

UK Biobank

Job description

Offer Description

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.

The role

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.

About you

You will have:

• A PhD in 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

At Southampton, you will join a supportive, internationally connected research environment at the forefront of genetic epidemiology, data science and precision medicine. We 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.

Working at the University of Southampton:

Check out the staff benefits and why you should join us at The University of Southampton.

• A standard DBS check is required for this role.