Research Fellow in Genetic Epidemiology and Data Science

University of Southampton; Southampton; On-site; GBP 36,000 - 48,000; Full time

Job summary

University of Southampton invites applications for a three-year, full-time Research Fellow in genetic epidemiology and data science. Based at University Hospital Southampton, you will integrate organ-health measures with large-scale genetic data to identify diabetes subtypes and predict disease risk.

Collaborate across epidemiology, genomics, imaging, statistics and data science to advance precision medicine.

Qualifications

• PhD in genetic epidemiology or closely related field.
• Experience analysing large-scale genetic, epidemiological or biomedical datasets (ideally UK Biobank).
• Strong statistical and computational skills with R and/or Python; Linux-based computing experience.
• Experience conducting GWAS and Mendelian randomisation analyses with quality control and interpretation.
• Experience applying machine learning to complex biomedical data.

Responsibilities

• Develop and apply ML approaches to identify diabetes subtypes from multi-omics and clinical data.
• Characterise clinical and genetic profiles of diabetes subtypes and assess reproducibility across populations.
• Conduct GWAS and Mendelian randomisation studies on organ-health traits and diabetes subtypes.
• Develop models to predict incident type 2 diabetes and progression using genetic and clinical data.
• Analyze large datasets (e.g., UK Biobank) with robust statistical methods and reproducible workflows.
• Publish findings and present at conferences; collaborate across multidisciplinary teams.

Skills

Machine learning for biomedical data

Statistical genetics

Clustering

Predictive modelling

Education

PhD in genetic epidemiology

PhD in statistical genetics

PhD in biostatistics

PhD in data science

Tools

R

Python

Linux

Job description

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.

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.

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

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.

As a university we aim to create an environment where everyone can thrive and are proactive in fostering a culture of inclusion, respect and equality of opportunity. We believe that we can only truly meet our objectives if we are reflective of society, so we are passionate about creating a working environment in which you are free to bring your whole self to work. With a generous holiday allowance as well as additional university closure days we are committed to supporting our staff and students and open to a flexible working approach.