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Wellcome / EPSRC Centre for Interventional and Surgical Sciences

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Multiscale Modelling, Simulation and Machine Learning Tools to Predict Graft Failure

A 3-year PhD studentship is available at UCL Department of Mechanical Engineering for an enthusiastic student interested in Multiscale Modelling and Simulation and Machine Learning Tools to Understand and Predict Graft Failure.

About 40% of lower extremity vein grafts occlude or develop significant stenosis already within the first year after implantation. Results for more complex procedures to the calf vessels have usually slightly worse prognosis, with resultant serious morbidity and mortality. In a clinical landscape with ever-increasing and more aggressive bypass procedures, the use of novel engineering simulation tools to understand venous adaptation to the arterial environment and the development of classification tools to understand patient-specific variability would help preventing the significant numbers of excess complications, mortality and cost of re-interventions. This project will create a flexible multi-scale modelling framework to engineer better outcomes for vascular patients undergoing bypass procedures (vein-grafts) and will harness the power of machine learning tools to understand individual variability, classify patients' risk and predict individual patients' outcomes.

Supervisor

Vanessa Diaz