AI and Agent-based Control

AI and Agent-based Control Artificial Intelligence and Agent-based Control for Improving Energy Network Resilience to Threats (2026) This PhD explores how AI can address major threats to energy infrastructure that cause widespread outages. It will develop machine learning and agent-based tools to predict and manage cascading failures—rare, high-impact events that can disrupt power, water and communications. Building on prior work such as the NTRM toolkit (https://github.com/sskazakos/NTRM), you will create simulations, analyse real datasets, and design resilience assessment tools. You’ll collaborate with industry, publish research, and gain skills in AI, energy systems modelling, and risk analysis. What you get This self-funded PhD project is available to UK and Overseas applicants who are able to self-fund or identify their own sources of funding. You will be supported when applying to any external sources. The University of Sussex believes that the diversity of its staff and student community is fundamental to creative thinking, pedagogic innovation, intellectual challenge, and the interdisciplinary approach to research and learning. We celebrate and promote diversity, equality and inclusion amongst our staff and students. As such, we welcome applicants from all backgrounds. Type of award PhD PhD project How can we leverage artificial intelligence to tackle modern serious threats to energy infrastructure that leave millions without power? This PhD project aims to investigate the use of Artificial Intelligence (AI) tools, including machine learning (ML) and agent-based control, for predicting, managing and improving the resilience of energy networks to disruption. AI tools will be used to predict the likelihood and impact of cascading failures. Cascading failures can lead to widespread electrical blackouts, typically characterised as High-Impact Low Probability (HILP) events, potentially leaving millions of people without energy, water or communications, risking lives, and costing £ billions. Prior knowledge of the occurrence of such HILP events can enhance the response of infrastructure operators, thus limiting their impact. You will build on prior research that has been done by the supervisor’s team on leveraging machine learning to predict large-scale blackouts, including the Network Theory Resilience Metric (NTRM) toolkit ( https://github.com/sskazakos/NTRM ). What you will do Develop a prototype toolkit, which can be used to assess the resilience of energy networks and link with industrial systems to extract data and advise on the response interventions. Work with datasets from energy networks, wherever possible. Build advanced simulation models utilising machine learning and agent-based control techniques. Collaborate with researchers and industry stakeholders. Publish in high-impact journals and conferences. Skills you will develop Energy network and complex systems modelling Artificial intelligence methods applied to infrastructure Resil