Research Fellow
| Posting date: | 19 February 2026 |
|---|---|
| Salary: | £35,608 to £46,049 per year |
| Hours: | Full time |
| Closing date: | 19 March 2026 |
| Location: | Warwick University, Coventry |
| Remote working: | Hybrid - work remotely up to 2 days per week |
| Company: | University of Warwick |
| Job type: | Contract |
| Job reference: | 111309-0226 |
Summary
We are seeking to appoint a Research Fellow to play a crucial role in our recently UKRI awarded project “AC/DC: Accessible CT Data Compression”. Specifically, to develop mathematical models for domain specific compression by exploiting their expertise in signal processing and statistical/predictive modelling. Experience with AI would be beneficial but not essential.
X-ray Computed Tomography enables non-destructive observation of an objects internal structure. From material phases of novel alloys to uncovering a fossils provenance it supports a broad spectrum of academic research. However, even modest operation of a single system generates >10TB of raw data annually which requires archiving for 10+ years. This had led to an unsustainable data accumulation globally, desperate for a solution.
This project will create the first accessible, domain specific, machine independent CT data compression methods. Your strong mathematical capability will help identify redundancies in the specific data structures, slashing storage requirements by up to 80% and directly lowering the carbon footprint of global research infrastructure. You will initially focus on lossless compression with predictor models and inspiration from video encoding. Subsequently, lossy methods will be developed with a focus on optimal compression with minimal acceptable data loss. These methods will be integrated into user friendly open-source compression software so everyone can benefit.
The project is joint with University of Cambridge and University of Portsmouth. It includes an annual two month secondment to Cambridge with all travel covered, to receive the experience of multiple research environments to develop the best data compression. There are a series of project partners including NASA, Rolls-Royce, and JLR who will be appraising the research as it evolves, ensuring the outcomes are exploited.
About You
An applied mathematician with an eagerness to identify patterns in data, and learn from fields adjacent to their knowledge. This position requires signal processing experience with a background in statistical modelling. Mathematical predictive modelling could be supported by context adaptive approaches.
The candidate must have Python programming experience. No previous experience with X-ray Imaging is necessary, but an understanding of image processing is beneficial.
Experience in AI in not necessary but would be advantageous. This project has a more AI focused role in an adjacent advertisement.
For details on the experience and skills required, please refer to the job description attached as a PDF below.
PhD Status
If you are near submission of your PhD, or have not yet had it conferred, any offers of employment will be made at Research Assistant level, at the highest spinal point of pay grade 5 (£34,610 per annum).
Upon receipt of evidence confirming the successful award of your PhD, you will be promoted to Research Fellow, at the lowest spinal point of grade 6 (£35,608 per annum).
X-ray Computed Tomography enables non-destructive observation of an objects internal structure. From material phases of novel alloys to uncovering a fossils provenance it supports a broad spectrum of academic research. However, even modest operation of a single system generates >10TB of raw data annually which requires archiving for 10+ years. This had led to an unsustainable data accumulation globally, desperate for a solution.
This project will create the first accessible, domain specific, machine independent CT data compression methods. Your strong mathematical capability will help identify redundancies in the specific data structures, slashing storage requirements by up to 80% and directly lowering the carbon footprint of global research infrastructure. You will initially focus on lossless compression with predictor models and inspiration from video encoding. Subsequently, lossy methods will be developed with a focus on optimal compression with minimal acceptable data loss. These methods will be integrated into user friendly open-source compression software so everyone can benefit.
The project is joint with University of Cambridge and University of Portsmouth. It includes an annual two month secondment to Cambridge with all travel covered, to receive the experience of multiple research environments to develop the best data compression. There are a series of project partners including NASA, Rolls-Royce, and JLR who will be appraising the research as it evolves, ensuring the outcomes are exploited.
About You
An applied mathematician with an eagerness to identify patterns in data, and learn from fields adjacent to their knowledge. This position requires signal processing experience with a background in statistical modelling. Mathematical predictive modelling could be supported by context adaptive approaches.
The candidate must have Python programming experience. No previous experience with X-ray Imaging is necessary, but an understanding of image processing is beneficial.
Experience in AI in not necessary but would be advantageous. This project has a more AI focused role in an adjacent advertisement.
For details on the experience and skills required, please refer to the job description attached as a PDF below.
PhD Status
If you are near submission of your PhD, or have not yet had it conferred, any offers of employment will be made at Research Assistant level, at the highest spinal point of pay grade 5 (£34,610 per annum).
Upon receipt of evidence confirming the successful award of your PhD, you will be promoted to Research Fellow, at the lowest spinal point of grade 6 (£35,608 per annum).