Research Associate/Fellow (Fixed term)

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Computer Science
Location:  Jubilee Campus
Salary:  £33,966 to £45,585 per annum, (pro rata if applicable) depending on skills and experience (minimum £33966 with relevant PhD). Salary progression beyond this scale is subject to performance.
Closing Date:  Sunday 07 January 2024
Reference:  SCI519123

Deep learning has the potential to revolutionise medical imaging. In this project we are exploring the optical and computational technologies required for non-invasive deep 3D whole body imaging using light. This will require new laser sources, new detectors and fast and efficient methods for correcting for scattering and aberrations deep in tissue sample.

This new position will contribute to a large multi-disciplinary, multi-institutional, EPSRC funded project. This role will involve the development and training of new deep learning approaches based on modern transformer networks that seek to detect the aberrations present in image samples for iterative feedback to the imaging system. We will also make use of diffusion probabilistic models for iterative image improvement under complex noise conditions.

In Computer Science the role holder will work with Dr Michael Pound and Dr Andrew Parkes, and be expected to work closely with partners Prof Amanda Wright and Prof Mike Somekh in the Faculty of Engineering at Nottingham, who will be developing the optical technologies required for non-invasive deep 3D imaging using (infra-red) light. 

The role holder will also work in collaboration with the wider project team at the Universities of Edinburgh and Southampton. More details can be found at https://www.nottingham.ac.uk/go/x-ray .

Candidates are expected to have a Ph.D. (or close to completion), in a computer vision or a deep learning-related subject. They should also have strong programming skills, excellent team-working skills, excellent report writing and communication skills. They should be highly-motivated with the ability to set and meet deadlines appropriate to the progress of the project. Experience of the most recent deep learning approaches such as Transformers, neural network attention, and Diffusion probabilistic models would be an advantage. Experience of applying machine learning to the field of adaptive optics would also be desirable.

This is a full-time (36.25 hours), fixed term post to 1st May 2025. Arrangements for job share may be considered.

Informal enquiries may be addressed to Dr Michael Pound at [email protected]. Please note that applications sent directly to this email address will not be accepted.

Further details:

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