(position reference DC 12).
SPRING DN is implemented by an international consortium of universities, research institutes, and industrial and other non-academic partners, providing doctoral candidates with an interdisciplinary research and training environment. The network combines systems and control theory, formal methods, explainable AI, data-driven approaches, and human-centered design to address the resilience of interconnected cyber-physical critical infrastructures. Candidates will participate in international mobility, secondments, specialized training activities, and validation studies involving real-world use cases in the energy, water, and transportation sectors.
The project combines interdisciplinary research with structured doctoral training to form experts capable of working across academia and the public, private, and third sectors, with a focus on:
- Develop anticipatory defense, automated diagnosis, and real-time risk-aware response mechanisms for future Critical Infrastructures.
- Create a scalable, long-lasting library of resilience agents and supporting training materials for energy, water, and transport infrastructures.
- Bridge academia and industry by validating explainable AI, formal methods, and human-centered approaches in real-world industrial environments.
The selected student will be enrolled in either the Electrical Engineering or the Computer Engineering PhD program of the Department of Electrical and Computer Engineering (ECE) at the University of Cyprus (UCY), the top-ranking university of the country. The ECE Department conducts top-quality research, with its faculty securing prestigious projects including multiple ERC Grants, Horizon2020/ Horizon Europe RIA projects, and many more. The selected student will work under the supervision of Prof. Mathaios Panteli and Prof. Demetrios Eliades (https://www.kios.ucy.ac.cy/people_category/kios-faculty/).
The successful candidate will conduct research at the state-of-the-art facilities of the KIOS Center of Excellence. In particular, the focus will be to advance Digital Twin (DT) technology for enhancing the resilience of interconnected Critical Infrastructures (CIs), addressing current limitations related to scalability, interoperability, cascading failures, and real-time data integration. The research will develop a scalable DT framework combining formal methods, modelling and simulation, knowledge engineering, and adaptive AI to represent complex cyber-physical infrastructure systems. The research will investigate anticipatory self-adaptation, LLM-supported human-interpretable diagnostics, explainable AI, and real-time risk-aware incident response to support rapid, human-interpretable decision-making while maintaining system safety and performance during disruptions and recovery. Finally, it will develop a reusable library of intelligent software agents to provide resilience-as-a-service across multiple CI sectors, including energy, water, and transportation.
Requirements
Research Field: Engineering
Education Level: Bachelor Degree or equivalent
