PhD Position in AI-Driven Resilient RAN Optimization for 6G and O-RAN Architectures
Develop autonomous, AI-based radio resource management strategies for resilient 6G wireless networks.
Best for:Best for researchers with a strong foundation in wireless networks, machine learning, and a passion for developing resilient 6G communication systems.
Overview
Ghent University's IDLab is seeking a motivated PhD candidate to join the Intelligent Wireless Networking cluster. The research focuses on developing decentralized, self-healing Radio Access Network (RAN) optimization strategies within the O-RAN framework for 6G systems. The successful applicant will investigate agentic AI and reinforcement learning to ensure network resilience against jamming, interference, and infrastructure failures. This role involves both theoretical modeling and practical validation using advanced simulation tools and in-house wireless testbeds. The position offers a collaborative environment with opportunities to contribute to high-impact publications and mentor master's students.
What you'll do
- Study state-of-the-art techniques in next-generation O-RAN based wireless networks including 6G and Wi-Fi.
- Design and implement intelligent techniques for robust RAN optimization and cross-technology coordination.
- Develop proactive spectrum management strategies to mitigate interference and jamming.
- Apply Agentic AI and Reinforcement Learning to build autonomous, decentralized control loops.
- Validate and benchmark resilient RRM solutions using simulation tools and in-house wireless testbeds.
- Write high-quality research papers for top-tier journals and international conferences.
- Assist in limited educational tasks within the research group.
- Supervise master's theses related to the research domain.
Requirements
Degree required: Master's degree in Computer Science Engineering, Information Engineering Technology, Electrical Engineering, or a related field.
Degree offered: PhD
Language: English; C1 CEFR level.
Instruction: English
- Strong background in wireless and mobile networks.
- Proficiency in machine learning, specifically Reinforcement Learning or decision-making agents.
- Excellent coding skills in Python and C++.
- Familiarity with wireless network simulation tools like ns-3 or prototyping frameworks.
- Strong analytical and mathematical skills for system modeling.
- Fluent English (C1 CEFR level) in speaking and writing.
- Must hold or be about to receive a relevant Master's degree.
- Must be able to commit to a 4-year research period.
Funding & terms
How to apply
Interested candidates should submit a motivation letter, a CV, at least two reference contacts, a copy of their diploma, and study transcripts. Applications should be sent through the official university portal.
- Motivation letter
- CV
- 2 reference contacts
- Diploma copy
- Study transcripts
Contact
Adnan Shahid· Professor
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