Fully Funded PhD Researcher – AI-Native 6G Networks | Queen Mary University of London (QMUL) | London, United Kingdom

Job Title

Fully Funded PhD Researcher – AI‑Native 6G Networks | Queen Mary University of London (QMUL) | London, United Kingdom

Recruiting Company

Queen Mary University of London (QMUL) – School of Electronic Engineering and Computer Science

Job Location

London, United Kingdom

Job Type

Full-Time | Fully Funded PhD Studentship | Research Position

Application Method

Contact: a.masaracchia@qmul.ac.uk

Funding Information

• Funding: Full Tuition Fees (Home Rate - UK Residents)
• Annual Stipend: Approximately £21,874 per year
• Duration: 3 Years
• Start Date: April 2027
• Application Deadline: Contact the Supervisor for Further Details
• Research Area: Artificial Intelligence, 6G Networks, Wireless Communications
• Application Information: Online

Position Summary

Queen Mary University of London is seeking a highly motivated PhD candidate to join a cutting‑edge research project focused on AI‑Native Cross‑Layer Resource Allocation for Intelligent 6G Networks. This fully funded doctoral opportunity offers the chance to work at the forefront of artificial intelligence, wireless communications, and next‑generation mobile network technologies that will shape the future of intelligent and autonomous 6G systems.

Detailed Job Description

This PhD studentship will explore how AI-native network architectures and Hierarchical Multi-Agent Reinforcement Learning (HMARL) can transform the management and optimization of future 6G Radio Access Networks. The successful candidate will conduct advanced research into predictive network intelligence, cross‑layer optimization, and data‑centric AI techniques to improve network efficiency, reliability, and user experience. Working within the renowned School of Electronic Engineering and Computer Science at QMUL, the researcher will collaborate with leading academics and contribute to innovative solutions addressing some of the most challenging problems in wireless communications. The project combines theoretical research, algorithm development, simulation, and performance analysis, providing an excellent platform for candidates interested in pursuing careers in academia, telecommunications research, AI, or advanced wireless systems engineering.

Key Responsibilities

• Conduct original doctoral research in AI-native wireless network architectures and intelligent 6G systems.
• Design and develop advanced machine learning and reinforcement learning algorithms for radio resource management.
• Investigate Hierarchical Multi-Agent Reinforcement Learning (HMARL) approaches for autonomous RAN optimization.
• Develop predictive traffic forecasting and mobility modeling techniques for next‑generation wireless networks.
• Perform cross‑layer optimization research across radio, network, and service layers.
• Design simulation frameworks and conduct performance evaluations of proposed 6G solutions.
• Analyze large‑scale wireless network datasets to support data‑driven decision‑making and optimization.
• Publish research findings in leading international journals and conferences.
• Present research outcomes at academic, industry, and scientific forums.
• Collaborate with academic supervisors and research teams on cutting‑edge telecommunications and AI initiatives.

Required Qualifications & Skills

• Bachelor’s and/or Master’s degree in Telecommunications Engineering, Electronic Engineering, Computer Science, Artificial Intelligence, Data Science, Mathematics, or a closely related discipline.
• Strong academic background with demonstrated potential for high-quality research.
• Knowledge of wireless communications, mobile networks, or Radio Access Network (RAN) technologies.
• Understanding of machine learning, artificial intelligence, or reinforcement learning concepts.
• Programming experience in languages such as Python, MATLAB, C++, or similar.
• Strong analytical, mathematical, and problem‑solving abilities.
• Ability to conduct independent research and manage long‑term research objectives.
• Excellent written and verbal communication skills.
• Strong motivation to pursue a PhD in advanced telecommunications and AI‑driven network systems.
• Eligibility for Home Rate tuition funding as specified by the studentship requirements.

Nice‑to‑Have Skills

• Experience with reinforcement learning or multi‑agent learning frameworks.
• Knowledge of 5G Advanced, Open RAN, or emerging 6G technologies.
• Familiarity with AI‑driven radio resource management techniques.
• Experience with network simulation tools and wireless performance evaluation.
• Prior research experience resulting in publications, dissertations, or research projects.
• Understanding of optimization theory, stochastic processes, or network modeling.

Recruitment Pro Tip

For a competitive PhD application, demonstrate not only strong academic performance but also a clear research interest in AI, machine learning, and wireless communications. Highlight any research projects, publications, dissertations, programming experience, or practical work involving telecommunications, reinforcement learning, network optimization, or data analytics, and clearly explain how your interests align with the vision of intelligent AI‑native 6G networks.