Failure cascading modelling in interdependent power-communication cyber-physical networks
Eligibility: UK/International (including EU) graduates with the required entry requirements
Duration: Full-Time – between three and three and a half years fixed term
Application deadline: 15 January 2025
Interview date: Will be confirmed to shortlisted candidates
Start date: May 2025
For further details contact: Abdorasoul Ghasemi
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.
Funding
Tuition fees and bursary
Benefits
The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral and Researcher College which provides support with high-quality training and career development activities.
Entry requirements
- A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
PLUS
- The potential to engage in innovative research and to complete the PhD within 3.5 years.
- A minimum of English language proficiency (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component).
Additional specifications
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.
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 about the technical details of the project, please contact: Abdorasoul Ghasemi
In the first instance please submit your expression of interest via the button below with a supporting statement detailing your suitablity with evidence of the following:
• Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics.
• Knowledgeable in machine learning techniques (had successful courses or projects)
• Be proficient in programming (preferably in Python).
• Ideally familiar with mathematical modelling/network analysis/graph theory/system dynamics