Research Fellow

This role sits within CyberUnite, a €4 million HEA-funded all-island collaboration led by Queen’s University Belfast and the University of Limerick, with partners including MTU, UCC and Gas Networks Ireland, to strengthen the resilience of critical infrastructure across Ireland and Northern Ireland.

The post focuses on research into network security for critical infrastructure, such as gas, water, and electrical grids. This specifically concerns Cyber-Physical Systems and the complex integration of IT-OT environments, supporting CyberUnite’s mission to develop resilient and adaptive cybersecurity solutions for shared social and economic systems.

The successful researcher will apply expertise in programmable networking, network traffic analysis, and machine-learning to investigate traffic profiling and monitoring and intrusion detection on encrypted traffic in a zero-trust, programmable network architecture. A major focus is designing and implementing in-network machine-learning-based intrusion detection mechanisms based on collection of network telemetry/observability data in the CPS testbed.

The role involves close collaboration with networking researchers integrating the IDS with in-network access control and policy enforcement. The role also involves collaboration with AI researchers integrating the in-network IDS with endpoint AI detection and agentic response models.

This post is available for 30 months or until 30 June 2029, whichever is soonest. Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.

We are seeking an ambitious and collaborative researcher to join CyberUnite. You will have, or be about to obtain, a PhD in computer science, mathematics, engineering, or a related physical sciences discipline. You will bring strong research experience in cyber security and programmable networking, supported by a publication record in respected journals and conferences.

We are seeking candidates excited by the opportunity to work on network security challenges in complex critical infrastructure environments such as gas, water, and electrical grids. We are particularly interested in candidates with hands-on experience in network programming and network security systems design, along with solid software programming skills. Experience contributing to research that integrates machine learning methods with network security in the CPS domain will be highly valued.

You will be an excellent communicator who can explain complex ideas clearly, work effectively within a large multidisciplinary team, and build strong relationships with industrial collaborators across the consortium.

We are looking for someone who is organised, adaptable, and professional; able to balance competing priorities, demonstrate integrity, honesty and openness, and travel to meetings, partner visits, and conferences nationally and internationally when required.

This is not an exhaustive list. To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information.

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