Research Assistant / Research Fellow: Brunel University London is seeking a dedicated and innovative Research Assistant / Research Fellow to contribute to a groundbreaking project focused on developing a wireless network digital twin system for smart city logistics. This position offers an exciting opportunity to work within a highly ranked Department of Computer Science on cutting-edge technologies related to 5G/6G systems and machine learning algorithms.
Designation
Research Assistant / Research Fellow
Research Area
Wireless Network Digital Twin Systems, Smart City Logistics, Machine Learning, AI Algorithms
Location
Brunel University London, Uxbridge Campus, UK
Eligibility/Qualification
- For Research Assistant:
- A Bachelor’s degree (or equivalent) in Computer Science, Engineering, or a related field.
- Experience or coursework in machine learning and AI technologies preferred.
- For Research Fellow:
- A PhD (or equivalent) in Computer Science, Engineering, or a related field.
- Demonstrable knowledge of machine learning, large AI models, and publications in leading AI/ML conferences are desirable.
Job Description
The successful candidate will engage in collaborative research funded by EU/UK projects with a focus on:
- Performance analysis of 5G/6G systems.
- Developing machine learning-based algorithms for network planning and optimization.
- Establishing radio propagation models and conducting extensive performance analysis.
- Collaborating with industrial partners across the UK and EU to further project goals.
- Contributing to publications and presentations that disseminate research findings.
How to Apply
Interested candidates should upload their CV (including publications) and a cover letter summarizing their experience and achievements through the application system. For informal discussions, candidates may contact Professor Kezhi Wang at Kezhi.Wang@brunel.ac.uk.
Last Date for Apply
Applications will be accepted until January 9, 2025.
Brunel University London is committed to maintaining a diverse and inclusive workforce. We encourage applicants from all backgrounds to apply and actively seek to employ individuals who are under-represented in our workforce.
