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ABOUT THE ROLE

This is a unique opportunity for an early career biomedical engineer wishing to develop their research with a multidisciplinary focus, working at the intersection of Artificial Intelligence, data science and biomedical modeling. This post is funded by a UKRI Project Grant.
You will be part of CVD Net and will work collaboratively with other institutions including the Alan Turing Institute, University of Sheffield and University of Nottingham to design and build cardiac digital twins from health data to accurately track changes to disease progression and responses to treatment for patients with pulmonary arterial hypertension (PAH).
You will also be part of the Cardiac Electro-Mechanics Research Group (CEMRG) at Imperial College London which applies statistical, machine learning and simulation approaches to combine experimental and clinical data with physics and biology to study the physiology, pathology, diagnosis and treatment of the heart.

WHAT YOU WOULD BE DOING

In this post, the you will will work at the interface of artificial intelligence, data science, and biomedical modelling to accelerate the creation and validation of cardiac digital twins. Your work will contribute to the advancement of digital twin technology in healthcare to enhance cardiovascular health research and improve personalized treatments.
You will facilitate the integration of multiple datasets, including imaging, clinical, and demographic information and develop and apply machine learning techniques to accelerate simulations and extract insights from complex model outputs.