Resilient Interdependent Multilayer Critical Infrastructure Networks (2026)

About the Project

How can we prevent cascading failures that leave millions without power, water, or communication?

This PhD explores cutting-edge approaches to understanding and strengthening the resilience of interconnected critical infrastructure systems, at a time when climate change, cyber threats and global disruptions are increasing system vulnerability.

Interdependent critical infrastructure, such as power, gas, telecommunications, water, transport and other networks, can be vulnerable to emerging threats like climate change, pandemics and cyber-terrorism, potentially leaving millions of people without energy, water or communications, risking lives, and costing £ billions. The root cause of the widespread nature of these disruptions can often be traced to the interconnected and interdependent structure of these infrastructure systems, which resembles a “network of networks”, otherwise referred to as multilayer networks.

What you will do

• Develop a prototype toolkit of holistic metrics, which can be used to assess the resilience of interdependent energy and telecommunications networks, simply by considering their fundamental structure, types of interactions and locations of potential interventions.
• You will build upon an existing open-source research platform (Network Theory Resilience Metric [NTRM] - https://github.com/sskazakos/NTRM), offering a strong practical starting point and opportunities to make visible, publishable contributions early in the PhD.
• Work with datasets from energy and telecom networks, wherever possible.
• Build more advanced simulation models for cascading failures.
• Collaborate with researchers and industry stakeholders.
• Publish in high-impact journals and conferences.

Skills you will develop

• Network science and complex systems modelling
• Python / data science applied to infrastructure
• Resilience and risk analysis for critical systems
• Interdisciplinary research (engineering + policy + systems)
• Experience with real-world datasets