Post-Doctoral Research Visit F/M Cross-Layer Machine Learning for Physical and MAC Layer Optimization in 5G Broadcasting

Inria Rennes; Rennes; On-site; EUR 35,000 - 45,000; Full time

Job summary

1000scholars invites applications for a postdoctoral researcher position focusing on AI-driven optimization for 5G Broadcast technologies. You will investigate algorithms across physical and MAC layers, addressing robustness, scalability, and real-world deployment constraints.

Working within the ROBIN project consortium, you will collaborate with INSA Rennes, University of Rennes, and industrial partners, publish results, and contribute to innovative demonstrations at the intersection of

Qualifications

• Strong background in 5G/6G wireless, including physical and MAC layer concepts.

Responsibilities

• Develop AI/ML-based optimization for 5G Broadcast physical and MAC layers.

Skills

5G/6G wireless

Machine Learning

AI techniques

Optimization methods

5G standards

Simulation tools

Python

C/C++

Research & publications

English proficiency

Tools

MATLAB

Python

NS-3

Job description

Context

Funding Context: ROBIN Project – Bpifrance i-Démo The Bpifrance i-Démo program, funded under the France 2030 investment plan, supports ambitious collaborative research, development, and innovation (R&D&I) projects with strong technological and economic impact. It aims to accelerate the development of breakthrough technologies and their transfer to the market by fostering collaboration between industrial companies and research organizations. The ROBIN project is funded under this program and brings together a consortium of two academic partners (INSA Rennes and the University of Rennes) and three industrial partners (TDF, Ateme, and ENENSYS). Together, they are developing innovative solutions for 5G broadcast technologies, with a particular focus on the delivery of broadcast services to 5G terminals.

Scientific Context

The evolution of digital broadcasting and mobile networks has led to the emergence of 5G Broadcast technology, enabling the delivery of high-quality multimedia services, including TV and video content, directly to 5G-enabled terminals. This technology relies on a combination of advanced terminal architectures, communication protocols, and standardization frameworks defined by organizations such as 3GPP. However, several scientific challenges remain to be addressed, including the optimization of physical and protocol layers, the design of efficient and robust 5G Broadcast base station architectures, and the development of adaptive transmission strategies capable of addressing heterogeneous and dynamic propagation conditions across indoor and outdoor environments. Artificial Intelligence (AI) and Machine Learning (ML) techniques offer promising approaches for improving system performance through data-driven optimization, intelligent resource management, and adaptive configuration. Nevertheless, challenges related to robustness, generalization, reliability, and computational complexity must be considered to ensure the deployment of AI-based solutions in real-world 5G Broadcast systems.

Assignment

The postdoctoral researcher will contribute to the development of advanced 5G Broadcast technologies by investigating AI-driven optimization approaches for improving system performance, robustness, and adaptability. The mission will focus on the design, evaluation, and optimization of communication strategies across the physical and MAC layers, considering heterogeneous deployment scenarios, including indoor and outdoor environments. The researcher will work on the development of intelligent algorithms for resource management, transmission optimization, and system configuration, while addressing challenges related to robustness, scalability, and real-world deployment constraints. The work will involve theoretical analysis, algorithm design, simulation, experimental validation, and collaboration with academic and industrial partners within the ROBIN project consortium.

Main activities

• Develop AI/ML-based optimization algorithms for 5G Broadcast physical and MAC layers.
• Analyze and optimize 5G Broadcast architectures, protocols, and transmission strategies.
• Evaluate robustness and performance of AI-driven solutions in heterogeneous indoor/outdoor environments.
• Conduct simulations and experimental validation on 5G Broadcast platforms.
• Collaborate with academic and industrial partners and contribute to scientific publications.

Skills

• Strong background in 5G/6G wireless communications, including physical and MAC layer concepts.
• Experience with Machine Learning (ML) and Artificial Intelligence (AI) techniques applied to communication systems.
• Knowledge of optimization methods, resource allocation, and adaptive transmission strategies.
• Familiarity with 5G standards, protocols, and broadcast/multicast communication systems is highly desirable.
• Experience with simulation tools (e.g., MATLAB, Python, NS-3, or equivalent) and performance evaluation of communication systems.
• Strong programming skills, particularly in Python and/or C/C++.
• Ability to conduct independent research, analyze scientific problems, and publish results in international journals and conferences.
• Good communication skills and ability to collaborate with academic and industrial partners.
• Proficiency in English (written and spoken).