Queen Mary University of London
5 days ago
PhD in AI-Native Cross-Layer Resource Allocation for Intelligent 6G Networks at Queen Mary University of London Queen Mary University of London in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded 3-year PhD. Funding includes UK Home rate tuition fees and a London stipend of approximately £21,874 per year.
Country
United Kingdom
University
Queen Mary University of London

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About this position
PhD opportunity at Queen Mary University of London in AI-Native Cross-Layer Resource Allocation for Intelligent 6G Networks.
This fully funded doctoral project sits at the intersection of artificial intelligence, 6G wireless networks, reinforcement learning, and telecommunications. The research will investigate AI-native architectures and hierarchical multi-agent reinforcement learning (HMARL) for predictive and cross-layer optimisation of future 6G Radio Access Networks (RAN).
Key themes include AI-driven radio resource management, multi-agent reinforcement learning for RAN optimisation, data-centric AI for wireless networks, and predictive traffic and mobility modelling. This is a strong fit for students with interests in machine learning for communications, network optimisation, and next-generation mobile systems.
Funding is described as fully funded, covering UK Home rate tuition fees plus a London stipend of around £21,874 per year. The project duration is 3 years.
No formal deadline is stated in the post. Interested candidates are advised to contact [email protected] for further details.
Funding details
Fully funded 3-year PhD. Funding includes UK Home rate tuition fees and a London stipend of approximately £21,874 per year.
What's required
Applicants should be interested in the intersection of AI, 6G, wireless networks, reinforcement learning, and telecommunications. The project specifically mentions AI-native architectures, hierarchical multi-agent reinforcement learning, predictive and cross-layer optimisation, radio access networks, radio resource management, data-centric AI, and traffic/mobility modelling. No explicit degree, GPA, or language-test requirements are stated in the post.
How to apply
Contact the listed email address for further details and to express interest. Share the opportunity with suitable candidates if relevant.
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