Research Fellow in Artificial Intelligence in Medical Imaging

Are you an early-career researcher who enjoys developing fundamental methods with impact in challenging problems in medical image computing?

Do you have a strong background in computer science, statistics, mathematics or physics and want to apply it to medical image computing?

Would you like to work with cardiologists, oncologists and endocrinologists and have access to massive clinical image databases?

Do you have a passion for combining computational algorithms, modelling and simulation to address key problems in medicine?

Are you ready to think out-of-the-box, innovate and find solutions to challenging problems?

The Centre for Computational Imaging and Simulation Technologies in Biomedicine ( CISTIB ), within the Faculties of Engineering and Medicine & Health , involves various academics and their research groups. CISTIB is a highly interdisciplinary team with expertise ranging from very algorithmic contributions to machine learning and artificial intelligence in medical imaging all the way to very translational research with impact cardiology, endocrinology, oncology and surgery. CISTIB focuses on computational imaging, image-based computational physiology, and modelling and simulation in biomedicine. CISTIB works in close cooperation with clinicians from various research centres from the University of Leeds and the academic hospitals of the Leeds Teaching Hospital Trust Foundation , the largest NHS Trust of the UK.

Clinical areas where CISTIB members have contributed to and made substantive innovations in the field are focused around the cardiovascular, musculoskeletal and neuro sciences, where they have developed diagnostic and prognostic quantitative image-based biomarkers and methods and systems for interventional planning and guidance. The centre hosts academic members from the University of Leeds and Research Fellows, Research Associates, PhD Students and Scientific Software Developers forming a cross-disciplinary team committed to clinical translation of their innovations.

The successful candidate will contribute to develop fundamental methods for deep learning and artificial intelligence for medical image analysis and computational physiology. You will work across multiple projects in CISTIB including, for instance, the InSilc project, where CISTIB in collaboration with groups across Europe seeks to develop an in-silico clinical trial platform for designing, developing and assessing drug-eluting bioresorbable vascular scaffolds (BVS). Another project funded by the Royal Academy of Engineering is the INSILEX project where CISTIB develops new generative models (graphical models, generative adversarial networks) to build virtual patients and virtual populations from very large datasets, like the UK Biobank, as part of our effort to realise the vision of in silico clinical trials of medical devices. A final example, is the BQ-Minded project, a Marie Curie Training network focused on developing methods for Quantitative MRI for estimating tissue microstructure parameters and where deep learning can help to solve ensuing inverse and parameter estimation problems.

Using your expertise in computing, mathematics and statistics, you will contribute to develop new methods for highly automated and robust solution of segmentation, registration, interpolation, data imputation, three-dimensional reconstruction, and classification, and prediction from large image (and non-image) databases. You will contribute technical and scientific developments that fulfil project objectives while ensuring the approaches stand themselves as contributing to the field of artificial intelligence and/or to statistical and deep learning.

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

Professor Alex Frangi , Diamond Jubilee Chair of Computational Medicine

Tel: +44 (0)113 343 5430 or email a.frangi@leeds.ac.uk