PhD Studentship: Artificial Intelligence and Agent-based Control for Improving Energy Network R[...]

Emerging Scholars Council; Brighton; On-site; GBP 19,000 - 23,000; Part time

Job summary

Emerging Scholars Council is seeking a PhD candidate to advance research on using artificial intelligence to predict and improve resilience of energy networks. The project will build on prior work using the NTRM toolkit, and will involve data from energy networks and simulated disruption scenarios.

The role emphasizes collaboration with researchers and industry partners, with results intended for publication in top journals and conferences.

Qualifications

• Experience modelling energy networks and resilience.
• Familiarity with AI methods applied to critical infrastructure.
• Ability to work with real-world datasets and derive insights.

Responsibilities

• Develop a prototype toolkit to assess resilience of energy networks and interface with industrial data sources.
• Utilise ML and agent-based control to model disruptions and responses.
• Collaborate with researchers and industry stakeholders to validate methods.
• Publish findings in high-impact journals and conferences.

Skills

Energy networks modelling

AI for infrastructure

Resilience & risk analysis

Real-world datasets

Education

PhD in a relevant field

Tools

NTRM toolkit

Job description

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
• Resilience and risk analysis for critical systems
• Experience with real-world datasets
• You will benefit from our researcher development training programme, to enable you to develop your skills as a researcher and ensure you have what it takes to be successful in your future career.

Further information on this approach can be found on the website of the Critical Infrastructure Resilience Network (CIReN): https://www.sussex.ac.uk/research/centres/critical-infrastructure-resilience-network/publications