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  EPSRC DTP Studentship: Digital blood twin: Disease signatures from common blood markers in deep longitudinal data towards AI-led enabled personalised healthcare and predictive medicine


   Faculty of Life Sciences & Medicine

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  Dr Hector Zenil  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

Blood tests are widely known to be highly clinically relevant and biologically informative. They help diagnose 70% of diseases from anaemia, to infection, cancer and autoimmune diseases. Despite common blood testing used in most medical diagnosis, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of key clinical and cellular measurements. This has led to silos and shallow understanding of longitudinal disease progression (from healthy states) and a poor understanding of the transition dynamics between health and disease. Current healthcare practices are reactive, and only use limited physiological and clinical historical information lacking longitudinal depth. In this project, we will analyse about 23 million datapoints from the deepest longitudinal sources of deep haematological and biochemical data from the largest NHS primary and secondary data repositories with Causal Machine Learning to unveil disease signatures for super early detection and understanding of disease emergence and progression. Gaining an understanding of an individual’s health and immune system over time using common blood markers currently collected by health systems has the potential to transform our understanding of health and disease, enable precision and personalised healthcare by means of a common medical test (haematology and biochemistry), without having to wait for genomics and other promises to be deployed at mass scale to deliver on precision and predictive medicine.

For further details and to apply please visit the studentship webpage.

Biological Sciences (4) Computer Science (8) Engineering (12) Mathematics (25)

Funding Notes

Each studentship is fully funded for 3.5 years. This includes tuition fees, stipend and generous project consumables.

Stipend: Students will receive a tax-free stipend at the UKRI rate of £21,237 (AY 2024/25) per year as a living allowance.

Research Training Support Grant (RTSG): A generous project allowance will be provided for research consumables and for attending UK and international conferences.

Tuition fees: Home.

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