Research Fellow in Deep Learning for Medical Image Synthesis (EPSCP1036)

Are you an early career researcher who enjoys finding innovative solutions to unmet clinical needs and translating deep learning in medical image analysis to the clinic? Do you have a background in machine learning and experience with working collaboratively with clinicians and clinical image databases? Do you have a passion for developing neural networks for computer vision? Are you ready to think outside 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 focuses on algorithmic and applied research in the areas of computational imaging, and image-based computational physiology modelling and simulation. CISTIB contributes in different areas of medical image computing and image-based biomechanical and computational physiology modelling. 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 neural systems, 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.

You will be part of the Cancer Research UK funded project and develop deep neural (DL) networks with an application to image synthesis. This fast track project aims at using deep learning to predict the appearance of brain tumour in plausible future low-field systems and evaluate if the images from these systems would be suitable for instigating follow up scanning on a conventional, high field clinical MRI system. The task will involve rapid deployment of neural networks to synthesis images of low from high field multi-modality brain MRI. The familiarity with the existing DL structures and ability to use transfer learning for rapid delivery of a working prototype is sought after.

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

Dr Ali Gooya, Lecturer in Computer Science

Tel: +44 (0)113 3431949 or email: a.gooya@leeds.ac.uk

Please note: If you are not a British or Irish citizen, from 1 January 2021 you will require permission to work in the UK. This will normally be in the form of a visa but, if you are an EEA/Swiss citizen and resident in the UK before 31 December 2020, this may be your passport or status under the EU Settlement Scheme.

Closing Date: 03 Feb 2021
Category: Research