Research Associate – Machine Learning for Wireless Networks in London Greater GB

Date:

Title: Research Associate in Machine Learning for Wireless Networks – London (Greater) (GB)

King’s College London is seeking applications for the position of Research Associate in machine learning for wireless networks. This role is part of an EPSRC-funded project called AUTONOMY, which focuses on developing smart solutions for cellular-connected unmanned aerial vehicle systems. The project involves collaboration between King’s College London, the University of Southampton, Queen Mary University of London, and industrial partners including Ericsson, Accelercomm, and Toshiba.

The ideal candidate will be a dedicated and innovative scientist with a PhD (or equivalent) in Telecoms, computer science and engineering, electrical/electronic engineering, or a related field. Experience in designing machine learning algorithms for wireless networks is preferred.

At King’s College London, the successful candidate will join a research-leading and multi-disciplinary team led by Dr. Yansha Deng. The role will be based in the Centre for Telecommunications Research group at the Department of Engineering. The Research Associate will also collaborate with project partners, members of the research group, and the UKRI TAS Hub.

This position is offered on a fixed-term contract of up to 24 months or until March 31, 2025, with a possibility of extension depending on the availability of funding.

In conclusion, King’s College London is recruiting a Research Associate in machine learning for wireless networks to contribute to the AUTONOMY project. This is an exciting opportunity to work on cutting-edge research in collaboration with leading institutions and industrial partners. Interested candidates who meet the qualifications are encouraged to apply before the deadline.

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Frequently Asked Questions (FAQs) Related to the Above News

What is the position being offered?

The position being offered is Research Associate in machine learning for wireless networks.

What is the purpose of the AUTONOMY project?

The AUTONOMY project aims to develop smart solutions for cellular-connected unmanned aerial vehicle systems.

Who are the collaborators on the AUTONOMY project?

The collaborators on the AUTONOMY project include King's College London, the University of Southampton, Queen Mary University of London, and industrial partners such as Ericsson, Accelercomm, and Toshiba.

What qualifications are required for this position?

The ideal candidate should have a PhD (or equivalent) in Telecoms, computer science and engineering, electrical/electronic engineering, or a related field. Experience in designing machine learning algorithms for wireless networks is preferred.

What department will the Research Associate be based in?

The Research Associate will be based in the Centre for Telecommunications Research group at the Department of Engineering at King's College London.

How long is the contract for this position?

The contract for this position is offered on a fixed-term contract of up to 24 months or until March 31, 2025, with a possibility of extension depending on the availability of funding.

Who will the Research Associate collaborate with?

The Research Associate will collaborate with project partners, members of the research group, and the UKRI TAS Hub.

How can interested candidates apply for this position?

Interested candidates who meet the qualifications are encouraged to apply before the deadline specified in the job posting.

Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.

Kunal Joshi
Kunal Joshi
Meet Kunal, our insightful writer and manager for the Machine Learning category. Kunal's expertise in machine learning algorithms and applications allows him to provide a deep understanding of this dynamic field. Through his articles, he explores the latest trends, algorithms, and real-world applications of machine learning, making it accessible to all.

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