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Postgraduate Study

1 course(s) offered in the MRC Biostatistics Unit

Biostatistics - PhD

The MRC Biostatistics Unit is an internationally recognised research department of the University of Cambridge specialising in statistical modelling and design with application to medical, biological or public health sciences.

Our PhD students are registered with the University of Cambridge. Students belong to one of the University's Colleges and are trained at our Unit at the University Forvie Site on the Cambridge Biomedical Campus at Addenbrooke's Hospital.

We have links with various departments, units and groups across the Biomedical Research Campus and the University more generally, including the University of Cambridge's Statistical Laboratory. We interact with various national and international organisations, such as the Alan Turing Institute, HDRUK and Pharma and Technology companies, and with other Universities.

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2 course(s) also advertised in the MRC Biostatistics Unit

Cardiovascular Research - PhD

From the Department of Public Health and Primary Care

The BHF 4-year PhD programme aims to train the next generation of researchers to develop innovative approaches for preventing cardiovascular diseases. Cardiovascular diseases are a major cause of illness and death worldwide. Preventing these conditions before they develop is one of the most effective ways to improve health. However, doing this requires new approaches that combine insights from biology, population health and data science.

This PhD programme will train a new generation of researchers to tackle cardiovascular disease prevention by equipping a new generation of cross-disciplinary researchers to work at the intersection of three frontiers:

  • Population health – analysing large-scale health data from diverse populations to understand disease risk and prevention

  • Systems biology – studying the biological processes underlying disease across multiple levels, from cells to whole organisms

  • Artificial intelligence and data science – developing computational methods to integrate and interpret complex biomedical data.

The programme brings together leading scientists from the Universities of Cambridge, Edinburgh, Imperial College London and Oxford, working in partnership with our academic or industry partners, including EMBL-European Bioinformatics Institute, Wellcome Sanger Institute, AstraZeneca, Novo Nordisk, Genomics Ltd, Owlstone Medical, ThermoFischer Scientific and Flagship Pioneering. By combining interdisciplinary training with real-world collaboration, the programme seeks to equip students with the expertise and networks needed to become leaders in cardiovascular disease prevention research.

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Population Health Sciences - MPhil

From the Department of Public Health and Primary Care

The MPhil in Population Health Sciences includes the academic disciplines of epidemiology, global health, health data science, infectious diseases, public health, and primary care research. In the first term, all students take five core modules in biostatistics, epidemiology, applied data analysis, public health and research skills. Students subsequently select at least six additional modules, either following a designated pathway in one of the named specialisation themes (epidemiology, global health, health data science, infectious diseases, public health, and primary care research) or following a more personalised pathway.

The course is open to postgraduates who wish to pursue a research, practice or leadership career in population health sciences.

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Department Members


Professor John Whittaker
Director

  • 40 Academic Staff
  • 20 Postdoctoral Researchers
  • 16 Graduate Students

http://www.mrc-bsu.cam.ac.uk/

Research Areas

  • Causal Mechanisms
  • Efficient Study Design
  • Precision Medicine
  • Population Health
  • Biostatistical Machine Learning
  • Linking genetic predictors of disease to biological mechanisms
  • Exploring shared heritability of traits and diseases
  • Design and analysis for early and late phase clinical trials
  • Trial emulation and digital outcome measures for trials
  • Mixture, multi-state, dynamic prediction and joint modelling
  • Estimating and optimizing treatments and treatment regimes
  • Data integration and Hospital utilization and optimization
  • Infectious disease modelling
  • Robust and scalable AI and ML for biomedical translation