Job Details

Job Title

R&D on Trustworthy Autonomous Security Management in Future Mobile Networks

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

The anticipated increasing complexity in operating and managing 5G and beyond networks and the ever-evolving threat landscape facing those networks call for enabling security self-managing capabilities, such as self-protection, self-healing, etc. Softwarization technologies (SDN/NFV) and emerging AI/ML techniques (e.g., Deep Learning, Reinforcement Learning, Federated Learning) are identified as key enablers to build up fully automated and smart Software-Defined Security (SD-SEC) solutions that cover the whole cybersecurity spectrum (i.e., Identify, Protect, Detect, Respond and Recover) while fulfilling the Service Level Agreement (SLA) requirements. Nevertheless, AI/ML models can be biased to make erroneous prediction and decision-making, potentially causing performance degradation and financial harm, as well as endangering SLA fulfilment and security guarantees. Three dimensions of trust are necessary to stimulate confidence in AI-based systems, namely, trust in datasets, trust in AI models, and trust in third party AI agent (in case of distributed AI and Federated Learning). Thus, mechanisms to build trust in AI-driven SD-SEC models are necessary to empower trustable smart 5G security management in conformance to legislation, verticals and standards’ security requirements.

Job Requirements

We are looking for highly-motivated students (or postdoc researchers) who are willing to conduct high-quality research works, developing efficient approaches and methods that leverage zero-touch management concept, AI/ML techniques, SDN/NFV technologies, Blockchain and Adversarial Machine Learning for empowering a trustable smart 5G/6G security management system. We are seeking candidates with good analytical skills and relevant experience in any of the following: artificial intelligence, machine learning, network security, and blockchain. The potential candidates should have expertise in programming languages (Python, C/C++, Java), ML frameworks (Tensorflow, Keras, PyTorch), cloud environments (Kubernetes, OpenStack), and SDN technologies (e.g., ONOS or OpenDayLight).


Should you have any inquiry, please contact Dr. Chafika Benzaid 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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