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  4. PhD Studentship in AI-based Cardiac Image Computing

PhD Studentship in AI-based Cardiac Image Computing

Application closing date: 31/03/2021
Start date: September/October 2021
Duration: 3 years
Funding: Available

Applications are invited for a fully-funded PhD Studentship starting in September/October 2021 to undertake research in the area of AI-based cardiac image computing.

Funding is for three years, covering student fees and, in addition, a tax-free stipend starting at £17,609 per annum. Applications are welcomed from candidates of all nationalities.

The PhD will research interpretable multi-modality AI fusion of cardiac data (images and metadata). Available datasets are often acquired with different modalities in isolation from each other (e.g., intravascular imaging modalities – OCT, IVUS – as well as echocardiographic, MRI, clinical metadata), and resulting AI methods suffer from this siloing. Instead of training two or more deep neural networks in isolation, we will explore new mechanisms to train them simultaneously by coupling their hidden layers. This will enable the learning of a shared representation with higher predictive power. We plan to extend recently proposed tensor and cross-modal attention methods to elucidate best practices in deep, multi-modality fusion.

Further, in clinical applications, it is important that AI-based predictions are interpretable and not represent black-box mechanisms. To address this challenge, the PhD may research into invertible deep neural networks and normalizing flows.

The PhD will be part of a “mini-CDT” composed of three PhD students studying different aspects of AI-based cardiac image computing.

Applicants should have, or be expected to obtain by the start date, a 1st class or 2:1 degree (or equivalent) in Computer Science, Mathematics, Engineering, or a related subject.  A research publication track record is desired, but not required for the role.

The student will be co-supervised by Prof Greg Slabaugh and Dr Martin Benning.  Slabaugh is a Professor of Computer Vision and AI in Electronic Engineering and Computer Science (EECS) at Queen Mary and also Director of the Digital Environment Research Institute (DERI) which brings together researchers from across QMUL faculties to drive new multi-disciplinary research in data science.  Benning is a Lecturer in Optimisation and Machine Learning at the School of Mathematical Sciences; his area of expertise is the theoretical and computational handling of inverse and ill-posed problems.  Queen Mary is a leading research-intensive Russell Group university, ranked 5th among multi-faculty institutions in the UK for research outputs (Research Excellence Framework 2014), and 110th in the world overall (Times Higher Education World University Rankings 2020).

Informal enquiries regarding the post may be made by email to: Professor Greg Slabaugh (g.slabaugh@qmul.ac.uk).

Applications should be made by following the online process at: https://www.qmul.ac.uk/postgraduate/research/subjects/computer-science.html. (“PhD – Full-time – (Semester 1 -  September Start)”).

The closing date for applications is: March 31st 2021. Interviews are expected to take place in the week beginning Monday 15th April 2021.