Research Fellow in Deep Learning in Medical Image Computing and Modelling

Are you an ambitious researcher who wants to set the theoretical foundations that solve clinical and industrial problems? Do you have a background in computer vision, medical image computing, machine and deep learning, biomedical engineering or computational multi-physics and multi-scale modelling? Are you willing to take up the challenge to working across disciplines and on real-world data? Are you passionate for combining computational algorithms, modelling and simulation in trailblazing research to create virtual patient populations and deliver in-silico trials in medical devices?

The Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), within the Faculties of Engineering & Physical Sciences and Medicine & Health, involves various academics and their research groups. CISTIB focuses on algorithmic and applied research in the areas of computational imaging, machine learning, deep learning, and computational physiology modelling and simulation, working with clinicians from the University of Leeds and the academic hospitals of the Leeds Teaching Hospitals NHS Trust. CISTIB is part of the Centres’ conglomerate conforming the broader Leeds Centre for Responsive HealthTech Innovation in response to the recent Government Leeds City Region Science & Innovation Audit recognising the regional industrial R&D focus on MedTech.

Within 10 years, we expect to have transformed MD design/evaluation by delivering these outcomes:

• Minimize animal/human suffering by reducing, refining, and replacing animal/human testing.
• Advance personalized treatment towards customized medical devices and precision medicine.
• Develop medical devices optimized for robustness to uncertainty and lifestyle profiles.
• Achieve a marked decrease in long-term device failures and an increase in beneficial patient outcomes.
• Improve cost-effectiveness by quicker execution at a fraction of the cost of a full-scale live trial.

You will support our work to address three main challenges: 1) Build Virtual Patient Populations using probabilistic modelling; 2) Model device-tissue interactions through multi-physics, physiological modelling; 3) Develop efficient schemes to run ensembles of virtual experiments through accelerated numerical solvers and physics-informed machine learning. We identified cardiovascular medical devices as the first exemplar scenario.

You can undertake innovative and high-impact research in one of the above areas: 1) deep learning for image analysis of cardiovascular population imaging (segmentation and modelling of cardiac chambers, valves, and vessels). These involve analysing datasets of tens of thousands of images in an automatic manner; develop data harmonization, image super-resolution, image imputation, generative image synthesis; and generative virtual population models. 2) modelling long-term response and failure models due to host organ-device interaction. Developing surrogate models for predicting long-term patient outcomes from technical device outcome measures. 3) Develop computational fluid dynamics, computational mechanics, and computational physiology methods accelerated using, amongst others, reduced-order models and physics-informed neural networks. Responsibilities will include developing new mathematical approaches to cardiovascular image analysis and computational physiology in the research areas outlined above; developing software implementation of these approaches in MULTI-. You will develop methods for highly automated and robust construction of image-based models of the cardiovascular system for subsequent multi-physics simulation of physiology.

Fixed term (31 October 2022 [grant funding]).

To explore the post further or for any queries you may have, please contact: Professor Alex Frangi, School of Computing, e-mail: a.frangi@leeds.ac.uk.