Job id: 072285. Salary: \xc2\xa336,532 - \xc2\xa341,299 pro-rata per annum, including London Weighting Allowance.
Posted: 28 July 2023. Closing date: 02 August 2023.
Business unit: Natural, Mathematical & Engineering Sci. Department: Engineering.
Contact details: Toktam Mahmoodi. Toktam.mahmoodi@kcl.ac.uk
Location: Strand Campus. Category: Research.
THIS VACANCY IS OPEN TO INTERNAL APPLICANTS ONLY
Job description
Applications are invited for a part-time position as Research Assistant/Associate in AI for communication Networks, and 6G mobile network design, including Edge AI, distributed AI, digital twining and energy efficiency in mobile and wireless networks. The position is at 20 hours per week part time at King\'s College London, and a fixed term contract until 28th February 2025.
The role will be part of a \xc2\xa312 million strategic investment of the UK government in project REASON. This is a joint project with academic partners, including Uni of Bristol, Strathclyde, and Southampton, and industrial partners, including Ericsson, Nokia, Samsung UK, Thales UK, and BT.
You will be a conscientious, innovative scientist who is conducting your PhD in Telecoms, computer science and engineering including electrical/electronic engineering or similar. Experience in designing machine learning algorithms for edge-enabled wireless networks is preferred.
At King\'s, you will join a research-leading and multi-disciplinary team REASON, including Dr Yansha Deng, Dr Vasilis Friderikos, Prof Toktam Mahmoodi, and Prof Osvaldo Simeone. You will be based in the Centre for Telecommunications Research (CTR) at the Department of Engineering. The successful candidate will also be expected to collaborate with the project partners and members of the research group.
This post will be offered on a fixed term contract until 28th February 2025. One part-time post at 20 hours per week.
Key Responsibilities
The roles will involve carrying out research in machine learning for 6G wireless networking, including edge networks, with the objective of developing task-oriented dynamic computation offloading and distributed intelligence for edge automation via simulations and prototypes. Research will need to be carried out to:
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