Research Associate in the Development of Data-Driven Tomographic Image Reconstruction for Low-Dose CT (Fixed Term)

Registartion website

Department of Applied Mathematics and Theoretical Physics

£32,816-£40,322

LE27686

Research

9 August 2021

7 September 2021

A position exists for a Research Associate position at the University of Cambridge to work on a project entitled 'Revolutionizing Medical Imaging (ReImagine) through Ubiquitous, Low-Dose, Automated Computed Tomography Diagnostic Systems' funded through the EPSRC Transformative Healthcare Technologies scheme in the Department of Applied Mathematics and Theoretical Physics.

This exciting project brings together the expertise in the groups of Prof. Carola-Bibiane Schönlieb (mathematical imaging and machine learning reconstruction methods), Dr. Sam Stranks (semiconductor devices and characterisation including halide perovskites), Dr. David Fairen-Jimenez (metal-organic framework materials development and characterisation) and Prof. Evis Sala (quantitative medical imaging methods) at the University of Cambridge, with those of Dr Sarah Bugby (medical gamma imaging) at the University of Loughborough, Prof. John Lees (radiation detectors) at the University of Leicester and Prof. Joost Batenburg (discrete tomographic reconstruction methods) at the University of Leiden, together with industry partners GE Research, Scintacor, Cheyney Design, and Immaterial.

Our vision is to revolutionize the use of X-rays for medical imaging through low-dose, high-resolution, inexpensive computed tomography (CT) scanners, where highly innovative hardware and software components are developed side-by-side to enable automated all-in-one pre-symptomatic diagnosis. This will be enabled by developing highly sensitive X-ray detectors using scalable halide perovskite semiconductors materials currently making impact as disruptive photovoltaic (PV) technologies for phase contrast X-ray imaging, in conjunction with AI-driven algorithms for image reconstruction, lesion detection and segmentation. This will realise quicker and more efficient healthcare delivery and prevent disease spread through extremely early detection of disease (e.g. those otherwise responsible for future pandemics) and for routine follow-up of oncology patients (e.g. early detection of cancer recurrence). The hardware will be interwoven with software and algorithm development, with both guided by clinical insight, industry and case studies to ensure fit for end users.

We are looking for a highly motivated and enthusiastic Research Associate (RA) to drive the development of AI based image reconstruction and diagnosis from low-dose tomographic imaging data. The successful applicant will join the Cambridge Image Analysis group (http://www.damtp.cam.ac.uk/research/cia/) and work closely with the other RAs, investigators and partners on the project.

The successful candidate will have obtained (or be close to obtaining) a Ph.D. in Mathematics or a closely related area. The ideal candidate will be experienced in one or more of the following areas: inverse problems, mathematical imaging, optimisation, deep learning, medical image analysis. A proven track record of scientific publication and presentation at international conferences is essential. In addition to working independently, the successful candidate should also work well in a team and help to promote a collaborative environment across the group and project team. Excellent communication, organisational and problem-solving skills, as well as willingness to achieve specific and ambitious milestones, are required. Experience in programming is essential (e.g. MATLAB / Python, Tensorflow/PyTorch).

Fixed-term: The funds for this post are available for 1 year.

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Please indicate the contact details of two academic referees on the online application form and upload a full curriculum vitae and a description of your recent research (not to exceed three pages). Please ensure that at least one of your referees is contactable at any time during the selection process, and is made aware that they will be contacted by the Mathematics HR Office Administrator to request that they upload a reference for you to our Web Recruitment System.

A complementary RA post is available in the Department of Chemical Engineering and Biotechnology, University of Cambridge, to develop the x-ray detectors. Both RAs will coordinate closely to attain the project goals including through project meetings involving project partners. https://www.jobs.cam.ac.uk/job/30819/

Start Date: 1 January 2022 or as soon as possible after.

Please quote reference LE27686 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. We particularly welcome applications from women and /or candidates from a BME background for this vacancy as they are currently under-represented at this level in our Department.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.