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Research Associate/Fellow (fixed term)

Computer Science

Location:  Jubilee Campus
Salary:  £30,487 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:  Wednesday 24 April 2024
Reference:  SCI084224

In this project we are developing AI-powered imaging techniques to help improve food security by building new imaging technologies for food crops and plants. Cutting edge imaging techniques are vital if we are to improve our understanding of plant growth, and breed stronger and higher yielding crops. Micro CT and laser ablation tomography are capable of imaging plant root and shoot development at cellular resolution. Manual measurement of these detailed images can take weeks for a researcher, and so AI techniques that can quickly and accurately measure these images are in huge demand.

Progress is best made by drawing on international expertise. This project will develop new AI solutions for high-resolution segmentation of plant cells in collaboration with partners in the UK, Canada and the US. During the project international visits will let us share knowledge and best practice, to learn about new ways of working, our partner’s imaging devices and AI developments. At Nottingham, we will develop new AI solutions of high-resolution segmentation in 3D datasets, across both LAT (Laser Ablation Tomography) and X-ray CT (Computed Tomography) modalities. Solutions across both imaging devices will represent a new and unique technology for the research community.

We are looking to fill a post-doctoral researcher position that will develop new deep learning technologies for the analysis of 3D microscopy images. A key focus will be on a world-leading laser ablation tomography technique, producing high-resolution colour images that will require the researcher to design and implement efficient training approaches for segmentation. We will link images between both LAT and CT, leveraging the best of both imaging types to extract accurate representations of plant root anatomy. We will work closely with our project partners at The University of Saskatchewan, CA ( and The Donald Danforth Plant Science Centre (

The candidate should have (or nearly completed) a PhD in a computer vision or a deep learning-related subject. The ability to work in an interdisciplinary team will be essential, ideally evidenced by previous work on interdisciplinary projects.  The ability to develop new CNN models/architectures, as well as use existing architectures, will be required. Knowledge of how these networks might be shared and deployed by scientists across institutions would be beneficial.

The candidate will primarily be based in the School of Computer Science, Jubilee Campus. They will be a member of the Computer Vision Laboratory, which has a strong track record in AI and computer vision for plant phenotyping, and access to a powerful GPU cluster to support experiments. The project includes substantial funding for travel, and we will expect the candidate to be able to visit our partners in Canada for a few weeks during the project.

The position is based in the School of Computer Science on our Jubilee Campus in Nottingham.

The post is offered on a fixed term contract until 13 April 2025 (it might be possible to extend the contract). Hours of work are full time (36.25 hours per week); however applications are also welcome from candidates wishing to work part-time (minimum 29 hours per week). Please specify in your application if you wish to work part time and the number of preferred hours. Job share arrangements may be considered.

Requests for secondment from internal candidates may be considered on the basis that prior agreement has been sought from both your current line manager and the manager of your substantive post, if you are already undertaking a secondment role.

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

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