PhD Researcher in Cascading Modelling: Coventry University is offering a prestigious scholarship for a PhD project focusing on the resilience of interdependent infrastructure networks against cascading failures. This research aims to develop a comprehensive model utilizing advanced simulation and statistical AI/ML approaches to enhance the resiliency of critical power and communication systems.
Scholarship Opportunity: Failure Cascading Modelling in Interdependent Power-Communication Cyber-Physical Networks
Designation
PhD Researcher in Failure Cascading Modelling
| Details | Information |
|---|---|
| Research Area | Resiliency in Infrastructure Systems |
| Location | Coventry University, UK |
| Application Deadline | 15 January 2025 |
| Start Date | May 2025 |
| Interview Date | To be confirmed to shortlisted candidates |
Eligibility/Qualification
- A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum of 60% mark in the project element or equivalent.
- Minimum overall module average of 60%.
- Proficiency in English (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component).
- Candidates should have a strong background in computer science or engineering, mathematics, and programming (preferably in Python).
- Familiarity with mathematical modelling, system/network analysis, and machine learning techniques is essential.
Description
Infrastructure networks such as power grids and communication systems are essential for modern society. This project focuses on understanding how failures unfold in these interdependent systems and the risk associated with their interconnected nature. The candidate will:
- Conduct a literature review on resilient interdependent systems.
- Develop a simulator for failure propagation scenarios.
- Analyze failure patterns using mathematical modelling and AI/ML methods.
- Contribute insights into the resilience of interdependent networks to inform their design and management.
How to Apply
Interested candidates should submit their expression of interest along with a supporting statement outlining their suitability for the project. The application must include evidence of:
- Background in computer science (or engineering), system engineering, or physics/mathematics.
- Knowledge of machine learning techniques.
- Proficiency in programming (preferably in Python).
- Familiarity with mathematical modelling/network analysis/graph theory/system dynamics (desired but not mandatory).
Shortlisted candidates may be tasked with a time-limited assignment, with top performers invited for an interview.
Last Date for Apply
15 January 2025
For further details, candidates can contact Abdorasoul Ghasemi directly.
