About the Project:
Future Network Services(https://www.nationaalgroeifonds.nl/overzicht-lopende-projecten/thema-veiligheid-en-digitalisering/6g-future-network-services
) is the Dutch 6G flagship project enabling a partnership between multiple Dutch Research Institutes/Universities and leading industry partners in the 5G/6G space. The FNS project bring together the best amongst Dutch academia, ICT industry, telecom operators, semiconductor companies and the public research organizations. The overarching aim of FNS project is to innovate breakthrough technologies for end-to-end 6G networks that includes intelligent antennas, joint communications and sensing , AI-driven radio access and core networking (RAN and CN) and the development of the so-called killer- applications. In the FNS project, different aspects of 6G will be investigated through three program lines(PL), namely; Intelligent Components (PL1), Intelligent Networks(PL2) and leading applications(PL3). By integrating all these aspects of 6G technologies, FNS project aims to develop intelligent, reliable and sustainable 6G networks. Apart from the research and development, preparation for the global standardization of 6G technologies is also an important aspect of FNS project.About the vacancies
Under the Intelligent Networks program line, we have two vacancies(one PhD/Postdoc position and one PhD position) at TU Eindhoven. The main objective for these positions is to work on the cloud-native, open, disaggregated and AI-centric design of RAN architecture and control aspects. There will be ample opportunities to collaborate within the FNS consortium representing researchers from the leading institutions at the forefront of 6G mobile networks. The key tasks include but not limited to:Position-1 (PhD/Postdoc)
-Multi-cloud 6G Networks Orchestration.
- Requirement specifications for the cloud- and AI-native 6G network architecture interfaces.
- 6G network architecture design considering multi-cloud (public/private) implementation with focus on interoperability.
- Resource allocation for Network Slicing in multi-cloud 6G networks considering disaggregated deployment of RAN functionalities, e.g., distributed massive MIMO.
AI/ML model optimization for 6G Networks
- Optimized AI/ML induction in 6G network design.
- Conflict resolution among different AI/ML models optimizing RAN, e.g., considering different Open-RAN xAPPs with conflicting objectives on RAN parameter tuning.
- Development of AI/ML model testing framework for safe deployment of AI/ML models in 6G RAN.