Postdoctoral Research Assistant in Federated Learning in Medical Image Analysis
Department of Engineering Science, Institute of Biomedical Engineering, Old Road Campus Research Building, Headington, Oxford, OX3 7DQ
Grade 7: £33,309 – £40,927 per annum
We are seeking a full-time Postdoctoral Research Assistant to join the Oxford Biomedical Image Analysis Laboratory to play a pivotal role as part of the Oxford component of an international collaboration with the Hong Kong based Centre for Cerebro-cardiovascular Health Engineering (COCHE). This is a rare opportunity to join an internationally leading inter-disciplinary team conducting research on novel computational methods for analysis of biomedical images and early-stage clinical translation in collaboration with clinical partners. The post is available for three years.
COCHE has been established to develop new ways to treat cardiovascular disease (CVD) with innovative technologies allowing early prediction and intervention, so people can stay a step ahead of the disease. The developed technologies aim to reduce the human cost, as well as alleviate both the financial and social burden on healthcare systems worldwide. The Oxford-based imaging project associated with COCHE concerns the development of the underpinnings of a simple-to-use low-cost ultrasound device for checking the health of the fetal heart.
You will be responsible for design and testing of federated learning algorithms for fetal heart structure detection and characterization models from data based at different clinical sites. You will also assist a Senior Researcher and clinical fellow on the design of simple to use video acquisition protocols and model validation experiments.
You should have a relevant PhD (or be near completion) in machine learning, computer vision or biomedical image analysis together with relevant experience. You should also have experience of original deep learning in imaging algorithm design and evaluation, as well as knowledge of federated learning. Excellent programming skills and experience with deep learning toolkits are also essential, as well as the ability to prioritise work to meet deadlines.
Informal enquiries can be directed to Professor Alison Noble email@example.com.
Only online applications received before midday on Friday, 26 November 2021 can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests, CV and the details of two referees as part of your online application.
The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.
Contact Person :
Professor Alison Noble
Vacancy ID :
Contact Phone :
Closing Date & Time :
Contact Email :
Click on the link(s) below to view documentsFilesize