Job Details

Job Title

R&D on AI-Synthesized Deterministic Networking for 6G Communications

Job Type

Master Thesis Work, Doctoral Thesis Work, or Postdoctoral Research

Job Start Time

Immediately available

Job Duration

Negotiable, Depends on the Job Type

Place of Work

Centre for Wireless Communications (CWC) – Networks and Systems, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland.

Job Background

Ultra-Reliable and Low-Latency Communications (URLLC) were considered as one of the key application scenarios in the 5G mobile communication systems with the requirement of extremely low latency and reliability (around 1 ms latency and up to 99.99999% reliability). Nevertheless, 5G URLLC does not fulfill all the Key Performance Indicators (KPIs) of diverse mission-critical applications like industrial automation, intelligent transportation, telemedicine, Tactile Internet, Virtual/Augmented Reality (VR/AR), and Meta-Universe. To provide satisfactory services to these applications, the concept of Deterministic Networking is gaining lots of momentum in 6G communication systems to meet the additional strict requirements of the emerging mission-critical applications. New technologies and novel theories need to be developed to satisfy the following objectives: 1) deterministic wireless throughput with high spectrum efficiency; 2) deterministic Quality of Service (QoS), e.g., bounded Age of Information, round-trip delay/jitter, and zero packet loss; 3) ultra-reliable and secure data delivery considering the network failure and attack; 4) high network availability and sustainability; 5) E2E network orchestration and performance guarantee by the coordination of multiple technologies or administration domains. The research on the adoption of DetNet in mobile networks is in its infancy and a myriad of challenging open issues, e.g., dynamic scheduling algorithm for time-sensitive flows, distributed learning architecture for time-sensitive flow configuration, scheduling and management, lie ahead.

Job Requirements

We are looking for highly-motivated students or postdoc researchers who are willing to acquire new knowledge/skills, master new technologies, and conduct high-quality research works, developing efficient approaches and algorithms that leverage DetNet, AI/ML techniques, SDN/NFV technologies, to facilitate the deterministic flow scheduling. We are seeking candidates with good analytical skills and relevant experience in any of the following: network switching and routing, software-defined networking, mathematical optimization, and machine learning. The candidates with expertise in programming languages (Python, C/C++, Java), ML frameworks (PyTorch), network simulators (Omnet, NS3) are always preferred.


Should you have any inquiry, please contact Dr. Hao Yu, at

Application Form

Full Name


Email Address


Short Bio


Curriculum Vitae


Motivation letter


Research Vision on the Topic

(Mandatory for Postdoc researcher application, optional for master and doctoral student application)  

Recommendation Letters

(Merge at least three recommendations in one pdf file, mandatory for Postdoc researcher application, optional for master and doctoral student application)  
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