Postdoctoral Deep Learning Scientist for Cardiovascular Imaging

We are seeking a highly motivated Postdoctoral Researcher to join the Division
of Cardiovascular Medicine in close interaction with the Big Data Institute,
and work closely with an interdisciplinary team of machine learning
scientists, MR scientists (Prof. SK Piechnik) and cardiologists (Prof. VM
Ferreira). Recent deep learning breakthroughs have provided a new perspective
to rethink contrast enhancement in medical imaging. You will develop novel
generative AI to enhance CMR without intravenous contrast, to detect
myocardial pathologies (especially diffuse fibrosis) beyond the current
diagnostic capabilities of cardiovascular imaging.





Your responsibilities will include making a significant contribution to deep
learning methodology for cardiovascular imaging, by developing novel deep
learning algorithms, especially deep generative models, to unveil and assess
pathological signals in CMR imaging. You will also develop and implement the
latest deep learning models for CMR imaging and data analysis, using
programming languages such as Python, TensorFlow, Keras and PyTorch.





You are required to hold or be close to complete a higher degree (DPhil/PhD)
in a relevant area of research and have strong deep learning and machine
learning programming skills. Experience in CMR image processing, good
understanding of CMR scanning protocols would be desirable.





This is a full-time appointment on a fixed term contract for 3 years funded by
BHF and you will be based at the University of Oxford Centre for Clinical
Magnetic Resonance Research (OCMR), Level 0, John Radcliffe Hospital, Oxford,
OX3 9DU. You will also have access to computing resources, facilities and
networking at the Oxford Big Data Institute.





Applications for this vacancy are to be made online; you will be required to
upload a CV and supporting statement (up to 2 pages) which explains how you
meet the selection criteria for the post.





Only applications received before 12.00 midday on 10th May 2024 can be
considered. Interviews are scheduled take to place on 31st May 2024.





The University is an Equal Opportunity Employer