Research Assistant (MSCA) with PhD Full Scholarship - 16044

Position Title: Research Assistant (MSCA) with PhD Full Scholarship on Networked Scalable Learning – 16044

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

Location: Brunel University London, Uxbridge Campus

The University will waive PhD international tuition fees.

Contract Type: Fixed term for 3 years.

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 the recently released 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.

This PhD scholarship is part of the EU HORIZON MSCA DN project ANT: Embedded AI Systems and Applications (https://ant-dn.eu/), which includes 18 PhD scholarships (4.9 M€).

Objectives: 1) To design networked scalable learning techniques to adapt to the increasing amounts of data, devices and network complexity; 2) To design network protocols to facilitate resource sharing in networked scalable learning; 3) To develop optimisation schemes for resource allocation and dynamic workload distribution.

Expected Results: 1) Networked scalable learning techniques that accommodate the dynamic topology, computing loads, data volume, resource availability and quality of service (QoS) requirements; 2) Network protocols to facilitate networked scalable learning; 3) Optimisation schemes for dynamic resource allocation and computing load distribution.

Planned secondments:

• ST (3 months, M16-M18): Networked scalable and continual learning in dynamic evolving environment, with Dr. D. Pau (KPI: joint paper).
• IMEC (4 months, M27-M30): Efficient data transfer protocols for networked scalable learning, with Dr. J. Famaey (KPI: joint paper).

Candidate profile: computer science, machine learning, electrical engineering, telecommunication engineering, applied mathematics or related areas.

Desirable skills/interests: machine learning, large language model, large AI model, signal processing, wireless communications, applied optimisation (the applicant should be proficient in at least one or two of the skills).

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.

Family allowance may be payable dependant on eligibility and evidence.

Research training and network fees are available up to £50,139.84 for 3 years.

For an informal discussion, please email Professor Kezhi Wang at Kezhi.Wang@brunel.ac.uk.

Closing date for applications: 28 March 2025.

For further details about the post including the Job Description and Person Specification and to apply please click the 'Apply' button.

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.

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