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Population Health Data Science: data mining within a novel platform (EpiGraphDB) being developed within the MRC IEU

About This PhD Project

Project Description

4-year PhD starting in October 2019. Closing date: 22nd March 2019.

We are seeking an enthusiastic postgraduate researcher with a strong interest in data science applied to health. The successful candidate will benefit from an internationally leading population health science research training environment as part of the MRC Integrative Epidemiology Unit (MRC IEU).

The studentship is part of the “Data Mining Epidemiological Relationships” programme at the MRC IEU, and the student will work with a wealth of data being generated on large population studies and integrated with data from a range of other sources.


Population health research is being transformed by the increasing wealth of complex data. New high-dimensional epidemiological datasets provide novel opportunities for systematic approaches to understanding the relationships between risk factors and disease outcomes. Moving beyond individual hypothesis testing to fully exploit the available data requires new approaches to data mining, including the development of machine learning approaches, natural language processing and application of ontologies/knowledge representation.

This studentship will work on data mining within a novel platform (EpiGraphDB) being developed within the MRC IEU. EpiGraphDB integrates genomic and population health data with information mined from the scientific literature and from a range of bioinformatic databases.


The aim of this studentship is to develop and apply data mining methods within a complex graph database representing epidemiological relationships between traits and other relevant biological information.

Objectives include:
1. Develop efficient methods for identification of informative sub-graphs utilising a range of methods
2. Develop approaches to knowledge representation within the graph database to support more effective data mining
3. Develop approaches to the triangulation of different types of evidence to prioritise findings
4. Identify novel risk factors for disease
5. Identify potentially spurious established risk factors

The student will be encouraged and supported in developing their own research ideas.

Research methods

A wide range of research methods may be used, including machine learning, network analysis, natural language processing, causal inference. The exact methods will depend on the background and interests of the successful candidate.

How to apply

Applications must be completed online at, choosing “ Faculty of Health Sciences” and the “Population Health Sciences” PhD programme, and entering “MRC IEU” as the fee payer.
You are encouraged to contact Tom Gaunt () prior to application to discuss the project and your research interests.

Funding and eligibility

The studentship offers a stipend at standard MRC rates (£14,777 in 2018/19), covers the cost of UK tuition fees and provides £1000 per year training costs. Standard MRC eligibility criteria apply. Only applicants from the EU and UK are eligible for this programme.


Prof Tom Gaunt, Professor of Health and Biomedical Informatics ()
Prof Peter Flach, Professor of Artificial Intelligence ()
Dr Ben Elsworth, Research Fellow in Bioinformatics and Causal Inference ()

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