PhD Studentship in Data-Driven Approaches for Type 1 Diabetes
Explore large-scale healthcare data to characterise disease heterogeneity and advance personalised care for type 1 diabetes.
Best for:Best for quantitative researchers with a background in data science, statistics, or biomedical informatics seeking to apply advanced analytics to healthcare challenges.
Overview
The University of Exeter, through the GW4 BioMed3 MRC Doctoral Landscape Programme, is offering a four-year fully funded PhD studentship focusing on data-driven approaches to type 1 diabetes. The successful candidate will leverage large-scale routinely collected healthcare data and advanced statistical methods to analyse disease trajectories, build predictive risk models, and examine treatment outcomes. This project aims to improve long-term clinical understanding and support risk stratification for personalized medicine. Candidates will join a collaborative training environment across the partnership with comprehensive financial support for fees and living stipends.
What you'll do
- Process and analyse large-scale routinely collected healthcare datasets to identify clinical patterns in type 1 diabetes.
- Develop and validate statistical and machine learning models for disease trajectory characterisation and risk prediction.
- Investigate factors associated with differential treatment responses and long-term health outcomes.
- Collaborate with clinical and academic researchers across the GW4 BioMed3 consortium.
- Disseminate research findings through peer-reviewed publications and academic presentations.
Requirements
Degree required: BSc or MSc degree in a relevant quantitative or biomedical discipline
Degree offered: PhD
Language: English
Instruction: English
- Strong background in data analysis, statistics, or quantitative health research
- Experience with programming languages suitable for data science such as Python or R
- Demonstrated ability to handle and clean complex datasets
- Familiarity with predictive modeling or statistical inference techniques
- Open to UK and International students
Funding & terms
How to apply
Deadline: 2026-10-21
Review the project details and funding eligibility criteria on the official university portal. Submit your application and required supporting documents through the external application link provided by the institution before the deadline.
- CV
- Cover letter
- Academic transcripts
- Reference letters
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