About . Brunel University London

Uxbridge UB8 3PH, UK

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Job description

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

Position Title: Research Assistant / Research Fellow - 16192

College/Department: College of Engineering, Design and Physical Sciences / Department of Computer Sciences

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 until 31 March 2027

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/UK projects (with funding support from Innovate UK, EU and UKRI) which aim to develop a wireless network digital twin system for smart city logistics and last-mile applications. It includes performance analysis for 5G/6G systems, developing machine learning-based algorithms, establishing a radio propagation model, and conducting network planning and optimisation. The successful candidate will also need to visit our industrial partner in the UK/EU, to do collaborative research and experiment. The candidate with the following knowledge is preferable: machine learning, large AI model, large language model (LLM), and Artificial intelligence generated content (AIGC). Preference will also be given to candidates with publications in leading AI/ML conferences.

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 [email protected]

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: 9 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: [email protected]

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.
Job type
Full-Time
Industry
Other
Job Sector
Graduate
Job Position
Research
City/Town
Uxbridge
Postal Code
UB8 3PH
Address
Uxbridge UB8 3PH, UK
Location
Southall
Post date
Closing date
Reference Number
16356067847862660309_crt:1787917084527

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