Job Description

Are you an early or mid-career researcher who wants to set the theoretical foundations that solve clinical and industrial problems? Do you have a background in computational fluid dynamics, modelling medical device-tissue interactions, computational multi-physics and multi-scale modelling? Are you willing to take up the challenge of working across disciplines and on real-world data? Are you passionate for combining computational algorithms, modelling and simulation in trailblazing research to deliver in-silico trials of medical devices?

We are looking for a Research Fellow to support our work by developing high fidelity models of cardiovascular fluid dynamics and device-flow interactions through multi-physics, physiological modelling. Experience in developing efficient schemes to run ensembles of virtual experiments through accelerated numerical solvers and physics-informed machine learning is a bonus. We have identified cardiovascular medical devices as the first exemplar scenario with an emphasis on valvular prostheses.

You will hold a BSc/MSc (or equivalent) degree in relevant areas (i.e. physics, engineering, computer sciences, mathematics, or statistics), and a PhD (or have submitted your thesis before taking up the role) in a relevant computational domain. A track record in research commensurate with level of experience; in depth knowledge in statistics, physics, machine learning, and/or engineering and maths in the broad sense and proficiency in computer programming is a must. Experience in medical imaging physics and analysis (e.g. MRI, CT, and US) and a good knowledge in statistics and/or Bayesian learning would be advantageous.

What we offer in return

And much more!  

To explore the post further or for any queries you may have, please contact:

Professor Alex Frangi, Diamond Jubilee Chair of Computational Medicine

Email: [email protected]