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Deep learning based analysis of clinical speech imaging data

   School of Electronic Engineering and Computer Science

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  Dr Q Zhang, Dr Marc Miquel  No more applications being accepted  Funded PhD Project (European/UK Students Only)

London United Kingdom Communications Engineering Computational Physics Data Science Electronic Engineering Software Engineering Systems Engineering

About the Project

PhD Studentship – Deep learning based analysis of clinical speech imaging data

Applications are invited for a fully-funded PhD Studentship starting in January 2022 to undertake research in deep learning based methods to analyse dynamic images of speech acquired using Magnetic Resonance Imaging (MRI) and x-ray video-fluoroscopy.

The PhD is part of a project grant funded by Barts Charity, led by Barts Health Clinical Physics in collaboration with QMUL. The successful candidate will be part of a multi-disciplinary team that includes surgeons, MRI physicists and computer scientists; and work alongside a clinical scientist also funded by the grant. Funding is for four years, covering student fees and, in addition, a tax-free enhanced stipend starting at £ 17,609 per annum. Applications are welcomed from home fees candidates (e.g. British, Irish, Settled EU).

Barts Clinical Physics has a long-standing collaboration with the North Thames Cleft network. As part of it, we have been working on developing novel MRI methods to image speech, as well as methods to analyse those images. The aim of this grant is to ensure that the technical developments made in the last few years can be fully translated into clinical practice and benefit the patients. It particular, we anticipate that by analysing the way the different organs move and their shapes, it will be possible to decide which type of surgery is best suited to each patient. This PhD will first focus on deep learning methods to analyse speech MRI and if possible, extend the work to the automatic analysis of the routine video-fluoroscopic images. A crucial part of the project is to extract each organ from the images (image segmentation) and this will build on some of our initial work. Clinically relevant measurements will then be implemented (e.g. shape analysis)

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

The student will be co-supervised by Dr Qianni Zhang and Dr Miquel. Dr Zhang is a senior lecturer in Computer Vision and Dr Miquel is a Consultant clinical scientist In MRI physics and honorary senior lecturer. They will bring together expertise from the computer science and image acquisition and analysis domains to support this cross-disciplinary research project. The student will be mainly based at the new Digital Environment Research Institute (DERI) but will be expected to spend some time at the School of Electronic Engineering and Computer Science and Barts Clinical Physics. 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 Dr Marc Miquel ([Email Address Removed]) or Dr Qianni Zhang ([Email Address Removed]).

How to apply

Applications should be made by following the online process at (“PhD Full-time Electronic Engineering – Semester 2 (January Start)”).

The closing date for applications is Friday 26th November 2021. Interviews are expected to take place in early December 2021.

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