PhD Candidate in AI Driven MAC Control for Integrated Terrestrial NTN 6G Networks

About this job

https://www.youtube.com/watch?v=_KHQjc4ndas&t=41s About the position Are you motivated to pursue a doctoral degree and contribute to cutting-edge research at the intersection of artificial intelligence and next-generation wireless communications like 6G? The Department of Information Security and Communication Technology (IIK) at NTNU has a vacancy for a PhD candidate in AI‑Driven & Self-healing MAC Control for Integrated Terrestrial-NTN 6G networks. The position is fully funded and will run for four years (100 % employment), including teaching duties. Your immediate leader is the Head of the Communication Technology Unit at the department of Information Security and Communication Technology. The workplace will be located in Trondheim. About the project Future 6G and beyond communication systems are expected to integrate terrestrial cellular networks with non-terrestrial platforms, including Low Earth Orbit (LEO) Satellites, High Altitude Platform Stations (HAPS), and Unmanned Aerial Vehicles (UAVs). While such integrated networks can provide ubiquitous connectivity and improved resilience, they also introduce significant challenges due to highly dynamic radio environments, heterogeneous backhaul infrastructures, mobility, resource constraints, and service disruptions. The core research focuses on the MAC layer and its techniques – the candidate will design, implement and evaluate an AI-driven MAC-control framework that self‑heals in integrated terrestrial-NTN (LEO, HAPS, UAV) 6G networks. The work will be hands-on and system-oriented; a closed-loop, verifiable prototype will be built, tested under realistic degradations and examined for different metrics such as latency, reliability, fairness and SLA‑violation. For example, the candidate will do some of the tasks including: AI-driven Link Adaptation and MAC control – develop AI-based mechanism that proactively anticipate and respond to rapidly changing conditions in integrated terrestrial and non-terrestrial networks (LEO satellites, HAPS, and UAVs) and to cascading radio access networks (RAN) failures/degradations. AI-Driven self-healing MAC – develop algorithms and techniques at the MAC layer for fast anomaly detection, failure prediction, autonomous recovery, and performance resilience. Service-aware MAC layer techniques – develop novel MAC-layer mechanisms that integrate queueing theory, traffic modelling, machine learning, and network-performance prediction for improving latency, reliability and fairness to support mission‑critical services. The proposed algorithms and mechanisms will be implemented and validated on the B5G Lab infrastructure at the IIK department, NTNU. The lab provides industry‑grade 5G equipment, open‑source RAN stacks, programmable OpenFlow/P4 switches and AI‑Boxes for fast inference, together with NetFPGA/DAG hardware for sub‑millisecond failure detection and a Timeseries‑DB/Grafana monitoring platform for closed‑loop testing. One or multiple satellite ground stations (e.g., Starlink, OneWeb) will be installed and connected to the core network soon to support NTN testing. Duties of the position Complete all the doctoral courses work, a condition to obtain the doctorate degree Carry out high‑quality research within the scope described above Publish enough scientific results in recognized venues and contribute to research disseminations Actively participate in the Networking Research Group and its activities Engage in international activities (conferences, research stays at foreign educational institutions) Teach and support learning activities in courses related to communications and networking Required selection criteria You must have a relevant master’s degree in wireless communications and mobile networks, computer networks and distributed systems, telecommunication engineering, computer science with a networking focus, or equivalent, with a strong background in theory, ML/AI, and mathematics Your master’s degree must correspond to a five-year Norwegian degree, where 120 credits have been obtained at master's level Master students can still apply, but the master's degree must be obtained and documented before starting the position, and no later than 1st of August 2027 You must have a strong academic background from your previous studies and have an average grade from your master’s degree study or equivalent education, which is equal to B or better compared to NTNU's grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you have a weaker grade background (not worse than C), you may be co

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