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Research Fellow in Deep Learning for Population Cardiac Analysis

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 medical image computing and experience with working collaboratively with clinicians and clinical image databases? Do you have a passion for developing statistical deep Bayesian methods for medical image analysis? 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 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 EPSRC funded BIANDA project and develop a full probabilistic atlas to accurately evaluate bi-ventricular motion abnormalities by integrating cardiac magnetic resonance (CMR) and metadata from a large population. The motion will be modelled as the spatiotemporal (3D+t) sequence of the heart shapes across the full cardiac cycle, extracted from cine CMR images. The atlas will be a Bayesian recurrent model that, given a sequence, it will predict a probabilistic distribution function (pdf) for the next status of the heart. More importantly, the pdf will be conditioned on the patient's metadata. The atlas will be derived from the UK Biobank CMR study aiming to scan n>100,000 patients by 2022. The training of the atlas will be pursued as the new releases of the data sets from the UK Biobank becomes available. The PI has an extensive experience in developing Bayesian and non-Gaussian statistical atlases from shapes and you will have the opportunity to closely teamwork with him. He has established collaboration with the clinical advisor for this study and has full access to the CMR data sets. 

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

Dr Ali Gooya, School of Computing

Tel: +44(0)113 343 1949 or email: a.gooya@leeds.ac.uk


Further information

The Faculty of Engineering is proud to have been awarded the Athena Swan Silver Award from the Equality Challenge Unit, the national body that promotes equality in the higher education sector. Our equality and inclusion webpage provides more information.

Location:  Leeds - Main Campus
Faculty/Service:  Faculty of Engineering
School/Institute:  School of Computing
Category:  Research
Grade:  Grade 7
Salary:  £33,199 to £39,609 p.a.
Post Type:  Full Time
Contract Type:  Fixed Term (Fixed-term 18 months (grant funding))
Release Date:  Monday 04 March 2019
Closing Date:  Tuesday 02 April 2019
Interview Date:  Wednesday 15 May 2019
Reference:  ENGCP1096
Downloads:  Candidate Brief  

The closing date for this job opportunity has now passed, and applications are no longer being accepted for this position

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Athena Swan Bronze Award
Equality and Inclusion - Everyone Included, Everyone Involved
HR Excellence in Research