Beyond-5G(B5G)/6G networks are expected to serve many applications (e.g., autonomous vehicles, remote surgery and digital twins) that pose stringent requirements on Telecommunications Network in terms of latency, data rates, reliability and availability. Thanks to the Network Slicing mechanism enabled by the network function virtualization (NFV) and Software Defined Networking (SDN) techniques, the B5G/6G networks exhibit immense potential for meeting these challenges posed by the stringent application requirements. In the last decade, there has been a significant progress in the area of network slicing for core networks and many new approaches/tools have been proposed.  However, the emerging dominance of Mobile Edge computing (MEC) and its strong coupling with Radio access networks (RANs) require a complete rethinking of the Network Slicing mechanism considering wireless access. In particular, the integration of RAN, core networks and MEC brings new challenges for enabling dynamic end-to-end network resource allocation. The main objective of this PhD position is to explore novel methods/techniques to meet the end-to-end network slicing requirements considering both the core networking and wireless access. In particular, the application of novel machine learning techniques such as Deep reinforcement learning (DRL) will be explored by the PhD candidate.

As part of the Advanced Networking Lab and the Center for Wireless Technology (CWTe), the project invites candidates with strong analytical skills and willingness to test novel approaches on our 5G Core, Backhaul and Radio Access Network laboratory testbeds.

Minimum qualifications:

1. A MSC degree in Electrical Engineering, Computer science or Telecommunications engineering.
2. A good understanding of Wireless Communications/Networking, Network Slicing and NFV concepts.
3. Knowledge of mathematical optimization techniques.
4. Programming experience in Python and/or C++.
5. A team player who is willing to work in a multi-cultural and international environment.
6. A good level of English knowledge skills.

Preferred qualifications:

1. Familiar with Open-RAN concept and knowledge of the open source platforms for 5G RAN implementation, e.g.  OpenAIRInterface.
2. Experience with Deep reinforcement learning libraries (Pytorch, Keras) and toolkits (e.g. OpenAI GYM).

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.