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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 should have the following knowledge: large AI model, large language model (LLM), and Raytracing tools. Preference will also be given to candidates with publications in leading AI/ML conferences.
College / Directorate
College of Engineering, Design & Physical Sciences
Department
Department of Computer Science
Full Time / Part Time
Part Time
Posted Date
16/05/2025
Closing Date
06/06/2025
Ref No
4840
Documents
* Job Description (Word, 100.06kb)
Position Title: Research Assistant – 16192
College/Department: College of Engineering, Design and Physical Sciences
Department of Computer Science
Location: Brunel University of London, Uxbridge Campus
Salary:Research Assistant: Grade R1 from £36,390 to £38,388 per annum inclusive of London Weighting with potential to progress to £39,432 per annum inclusive of London Weighting through sustained exceptional contribution. (Pro-rata for Part-time)
Hours: Part-time: 15 hours per week
Contract Type: Fixed term for 12 months
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 should have the following knowledge: large AI model, large language model (LLM), and Raytracing tools. 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 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: 6 June 2025
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.
Seniority level
* Seniority level
Entry level
Employment type
* Employment type
Part-time
Job function
* Job function
Research, Analyst, and Information Technology
* Industries
Higher Education
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