Active Public Health & Healthcare Physics & Astronomy

The HDRUK/Turing Wellcome PhD Programme in Health Data Science

In plain English

AI plain-English summary

The programme will train a new generation of researchers who can work across statistics, computing, and medicine to turn patient data into better healthcare. Health data science is held back by a shortage of people who can bridge these three worlds. Statisticians rarely understand clinical workflows; clinicians rarely write code; computer scientists rarely grasp the subtleties of medical data. This four-year PhD programme tackles that gap directly. It takes graduates with strong quantitative backgrounds and gives them a year of foundational training before they embark on a three-year thesis project. Each student is co-supervised by at least one quantitative expert and one health researcher, and projects with non-academic partners add a third supervisor from industry or the NHS. If it succeeds, the programme will create a cohort of leaders who can build and sustain the collaborative teams needed to analyse large-scale health datasets. That could mean faster diagnosis algorithms, more reliable predictions of disease progression, or better allocation of NHS resources. The impact is on the infrastructure of medical research itself—the people and methods that make data-driven discovery possible.

View original technical description
This four-year programme in Health Data Science includes one year of foundational training followed by a three-year thesis project. It will contribute to much-need capacity-building in health data science by bringing quantitative methods graduates into the health research sphere. Its underlying philosophy is that health data science requires a combination of expertise spanning three fundamental areas: statistical, computational and health sciences. This goes beyond the capacity of any single individual; impactful progress can only be made through highly collaborative communities led by resilient leaders who can challenge and overcome restrictive paradigms. Health data scientists must therefore be trained to operate in multi-disciplinary teams with each member an expert in one of the fundamental areas but conversant in all three. The proposed programme will have a UK-wide reach, drawing on the combined research expertise of two national research institutions. Each student will be co-supervised by at least one expert in quantitative methods and one in substantive health research. Co-funded projects with non-academic partners will include a third supervisor drawn from the partner organisation. Topic selection will be informed by exposure during year 1 to a wide range of problems and methodologies, with flexibility to switch topic and location for years 2-4.

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Researchers

Christopher Yau (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

EPSRC Centre for Doctoral Training in Data Science
EPSRC Centre for Doctoral Training in Healthcare Data Science
EPSRC Centre for Doctoral Training in Health Data Science
EPSRC Centre for Doctoral Training in Physical Sciences for Health (Sci-Phy-4-Health)
EPSRC Centre for Doctoral Training in Data-Driven Healthcare (DRIVE-Health)

Original classification

PhD Programme in Science (Basic)

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