Research Assistant/ Research Fellow - 16945

Job summary

The successful candidate will develop wireless communication optimisation algorithms and real-time AI inference models for digital healthcare applications. They will also conduct collaborative research and experimentation with industrial partners in the UK and EU.

Job details

The successful candidate will contribute to the EU projects (with funding support from Innovate UK and UKRI), which aim to develop wireless communication optimisation algorithms for digital healthcare applications and/or low-latency real-time AI inference models for markerless pose detection in healthcare applications. The successful candidate will also need to visit our industrial partner in the UK/EU to do collaborative research and experimentation. The candidate with the following knowledge is preferable: Agentic AI, large language models (LLMs), and Vision-Language-Action (VLA) models. Preference will also be given to candidates with publications at leading AI/ML conferences, such as CVPR, ICLR, and NeurIPS. College / Directorate College of Engineering, Design & Physical Sciences Department Department of Computer Science Full Time / Part Time Full Time Posted Date 16/07/2026 Closing Date 24/09/2026 Ref No 5181 Position Title: Research Assistant/ Research Fellow – 16945 College/Department: College of Engineering, Design and Physical Sciences/Department of Computer Science Location: Brunel University of London, Uxbridge Campus Salary Salary for Research Assistant: R1 Grade from: £37,118 to £39,144 per annum inclusive of London Weighting with potential to progress to £40,202 per annum inclusive of London Weighting through sustained exceptional contribution. (Pro-rata for Part-time) Salary for Research Fellow: R1 Grade from: £41,292 to £44,762 per annum inclusive of London Weighting with potential to progress to £48,557 per annum inclusive of London Weighting through sustained exceptional contribution. (Pro-rata for Part-time) Hours: Full-time Contract Type: Fixed term for 4 months or until 28 Feb 2027, whichever is earlier. Extension may be possible depending on the progress and subject to the availability of funding. Brunel University of London was established in 1966 and is a leading multidisciplinary research-intensive technology university delivering economic, social and cultural benefits. For more information please visit: https://www.brunel.ac.uk/about/our-history/home The Department of Computer Science at Brunel where this project will be conducted is ranked 3rd in the UK (2020-22) overall in the NTU Performance Ranking of Scientific Papers for World Universities and, for five years in succession, 1st in the UK for H-index and Highly Cited Papers (2018-2022). According to 2023 Shanghai Academic Ranking of World Universities (ARWU), Computer Science & Engineering at Brunel has been ranked 7th in the UK and a very respectable 101-150 position worldwide. Moreover, according to the 2023 Times Higher Education rankings, Computer Science is 17th in the UK and in the Top 200 worldwide. The successful candidate will contribute to the EU projects (with funding support from Innovate UK and UKRI), which aim to develop wireless communication optimisation algorithms for digital healthcare applications and/or low-latency real-time AI inference models for markerless pose detection in healthcare applications. The successful candidate will also need to visit our industrial partner in the UK/EU to do collaborative research and experimentation. The candidate with the following knowledge is preferable: Agentic AI, large language models (LLMs), and Vision-Language-Action (VLA) models. Preference will also be given to candidates with publications at leading AI/ML conferences, such as CVPR, ICLR, and NeurIPS. Please upload your CV (including publications) and a Cover letter summarising your experience and achievements in the application system. For an informal discussion, please email Professor Kezhi Wang at Kezhi.Wang@brunel.ac.uk We offer a generous annual leave package plus discretionary University closure days, excellent training and development opportunities as well as a great occupational pension scheme and a range of health-related support. The University is committed to a hybrid working approach. Closing date for applications: 24 September 2026 For further details about the post including the Job Description and Person Specification and to apply please visit https://careers.brunel.ac.uk If you have any technical issues please contact us at: hrsystems@brunel.ac.uk Brunel University London is fully committed to creating and sustaining a fully inclusive workforce culture. We welcome applicants from all backgrounds and communities, we particularly welcome applicants who are currently under- represented in our workforce. Documents JD - Research Assistant (Word, 119.71kb) JD - Research Fellow (Word, 116.32kb) Apply here Send to a Friend

What you’ll do

The successful candidate will develop wireless communication optimisation algorithms and real-time AI inference models for digital healthcare applications. They will also conduct collaborative research and experimentation with industrial partners in the UK and EU.

Requirements

Candidates should possess knowledge in Agentic AI, LLMs, and VLA models, with a preference for those having publications at leading AI/ML conferences. A strong research background in computer science or engineering is essential for this role.

Benefits

• Annual leave package • University closure days • Training and development opportunities • Occupational pension scheme • Health-related support

Listed skills

• Data analysis · Preferred
• Machine learning · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Wireless communication
• Optimisation algorithms
• Digital healthcare
• AI inference models
• Markerless pose detection
• Agentic AI
• Large language models
• Vision-language-action models
• Machine learning
• Computer vision
• Research
• Data analysis

Job areas

• Science & Research
• Technology
• Engineering
• Healthcare
• Education