Failure cascading modelling in interdependent power-communication cyber-physical networks Coventry University in United Kingdom

This exciting interdisciplinary project offers the successful PhD student the opportunity to advance knowledge on resilient interdependent energy networks at the intersection of energy networks, network science, and AI/ML by developing mathematical theory and implementing software packages. Coventry University invites applications from suitably qualified graduates for this fully funded PhD studentship, based in the Centre for Computational Sciences and Mathematical Modelling.

Introduction

Infrastructure networks, such as power grids and communication systems, are the lifelines of modern society. Resiliency is crucial for these networks in the face of rare extreme events. Moreover, as these networks become increasingly interdependent for enhanced functionality, their interdependence introduces a risk: a failure in one system could propagate and disrupt the functioning of another. Therefore, the resilience of the entire system to rare events is particularly important, as the risk associated with such events is high. To improve system resilience, it is essential to understand how failures unfold within the system and how interdependencies influence this process.

Project details

This project aims to develop a model to analyse the resilience of interdependent power and communication networks against cascading failures. The research will address the complexities of failure propagation between these two critical infrastructures using a combination of graph-based models, advanced simulation techniques, and statistical AI/ML approaches. This research will contribute to developing new insights into the resilience of interdependent networks, leading to more resilient systems that can anticipate, absorb, and recover from disruptions. The selected candidate will be expected to:

• Conduct a literature review on resilient interdependent systems.

• Develop a simulator to run scenarios of failure propagation, focusing on both random and correlated failures across the systems.

• Analyse failure patterns using mathematical modelling, including graph theory and AI/ML methods.

• Contribute to the future of resilient infrastructure by providing insights into the design and management of interdependent networks.

Additional requirements

The selected candidate must have a strong computer science (or engineering) and mathematics background and be proficient in programming (preferably in Python). A high interest in interdisciplinary research is essential.

• The candidate should be familiar with mathematical modelling and/or system/network analysis.

• A passion for solving real-world challenges related to infrastructure resilience and knowledge of machine learning techniques is essential.

• The candidate should be motivated to work independently and collaborate within a research-driven environment.

• Knowledge and previous experience in graph theory, network science, system dynamics, and network analysis is desirable.

• Shortlisted candidates may be given an assignment with a limited timeframe for submission, with top performers invited for an interview.

Entry criteria for applicants to PhD

• A bachelor’s (honours) degree in a relevant discipline/subject area with a minimum classification of 2:1 and a minimum mark of 60% in the project element (or equivalent), or an equivalent award from an overseas institution.
• PLUS
• the potential to engage in innovative research and to complete the PhD within 3.5 years
• An adequate proficiency in English must be demonstrated by applicants whose first language is not English. The general requirement is a minimum overall IELTS Academic score of 7.0 with a minimum of 6.5 in each of the four sections, or the TOEFL iBT test with a minimum overall score of 95 with a minimum of 21 in each of the four sections.

For further details please visit: https://www.coventry.ac.uk/research/research-opportunities/research-students/making-an-application/research-entry-criteria/

Cover Letter Requirements:

Applicants must demonstrate a clear understanding of the project description and the nature of the program, highlighting their education and previous experience related to the project. Candidates should also indicate whether they have used AI tools to prepare the cover letter or any related supporting documents.

How to apply

To find out more about the project, please contact: Abdorasoul Ghasemi: [email protected] or visit our website to complete and expression of interest for the project.