|Salary:||£38,826 to £42,087 per annum, including London Weighting Allowance|
|Placed On:||31st March 2023|
|Closes:||7th May 2023|
Building and Campus: Strand Building, Strand Campus
Contact: Yansha Deng, email@example.com
Hiring manager/s: Allie Gilbert, Jon Millwood
Applications are invited for a position as Research Associate in machine learning for wireless networks at the King’s College London. The position is full-time and fixed term for two years. The post is due to start on the 16th of Aug. 2023.
The role will be part of an EPSRC-funded project on Smart Solutions Towards Cellular-Connected Unmanned Aerial Vehicles System (AUTONOMY). This is a joint project among King’s College London, the University of Southampton, Queen Mary University of London and with industrial partners, such as Ericsson, Accelercomm and Toshiba. Currently, recruitment is underway for one PDRA at each institution.
You will be a conscientious, innovative scientist who has successfully completed a PhD (or equivalent) in Telecoms, computer science and engineering including electrical/electronic engineering or similar. Experience in designing machine learning algorithms for wireless networks is preferred.
At King’s, you will join a research-leading and multi-disciplinary team led by Dr Yansha Deng. You will be based in the Centre for Telecommunications Research group at the Department of Engineering. The successful candidate will also be expected to collaborate with the project partners and members of the research group and the UKRI TAS Hub.
This post will be offered on an a fixed-term contract of up to 24 months or until 31st March 2025 (with a high likelihood of an extension, depending on the availability of funding). This is a full-time post - 100% full-time equivalent
The role will involve carrying out research in machine learning for cellular-connected UAV (C-UAV) swarms, with the objective of developing and implementing full network automation via simulations and prototypes. The research will need to be carried out to:
The successful candidate will have a strong interest in at least one of these, and a keen interest in the other. The successful candidate will be able to develop skills in the exciting and emerging field of machine learning for future wireless networks.
The role may involve irregular hours, with intense periods of activity leading up to deadlines such as publications or deliverable milestones.
The key challenges on which the applicant will focus can be briefly defined as the following:
The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.
Type / Role: