PhD Studentship in Computer Science - Dynamic Validation of AI Systems in Digital Twins: A Real-Time Safety Framework for Critical Infrastructure Resilience

Award Summary

100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project costs will also be provided.

Overview

Research Project

Critical infrastructure systems, such as power grids, transportation networks, and water treatment plants, increasingly rely on AI-driven decision-making for efficiency and autonomy. However, these systems face unique safety challenges. Real-world conditions, including weather, cyberattacks, and equipment degradation, are unpredictable, causing AI behaviors to deviate from lab-tested performance.

Current digital twin technologies focus on predictive maintenance and optimization but lack frameworks to continuously verify AI safety in operational contexts. This project aims to develop a dynamic validation framework for AI systems using high-fidelity digital twins, enabling real-time stress-testing under simulated edge cases like cyber-physical attacks and sensor failures.

The Research Challenges

There exists a complex interplay of factors that present challenges in ensuring the resilience of AI in critical infrastructure. The main challenges are:

1. a) Dynamic environments: Real-world conditions are unpredictable. Environmental factors and cyberattacks can cause AI behaviors to deviate significantly from their lab-tested performance metrics.
2. b) Cascading failures: Errors in AI decisions can propagate across interconnected infrastructure, leading to catastrophic outcomes. A single failure point can trigger widespread system collapse in grids or transport networks.
3. c) Regulatory lag: Existing safety certifications, such as ISO standards, lack methods to validate AI systems in real-time, adaptive scenarios. There is a disconnect between static regulation and dynamic operational risks.
4. d) Verification Gaps: Current digital twin technologies lack the frameworks necessary to continuously verify AI safety in operational contexts, leaving systems vulnerable to unpredicted behaviors.

The proposed framework provides a comprehensive solution by designing resilience metrics to quantify AI safety, focusing on robustness, recoverability, and ethical compliance. To bridge digital twin simulations with physical systems, the project will deploy real-time monitoring tools enabling preemptive risk mitigation. Furthermore, it will embed regulatory rules, such as the EU AI Act, into digital twins to audit AI alignment with fairness and transparency standards. 

Supervision Environment

Extensive training will be provided on physics-informed digital twin development and critical infrastructure simulation. Training on formal verification methods (probabilistic model checking) and AI safety compliance (EU AI Act standards) will also be provided.

Student Applicant Skills/Background

The applicant should have a solid background in computer science or systems engineering. Knowledge of AI/ML algorithms and simulation environments is highly advantageous. A keen interest in critical infrastructure resilience, cyber-physical systems, and AI safety ethics is essential to align with the focus of this research. Additionally, candidates should demonstrate analytical thinking regarding safety certifications and regulatory compliance.

Number Of Awards

1

Start Date

1 October 2026

Award Duration

4 years

Application Closing Date

15 February 2026

Sponsor

EPSRC

Supervisors

Dr. Yinhao Li, Dr Dev Jha, Dr. Charith Perera 

Eligibility & How to Apply 

For eligibility criteria and how to apply please visit our website.

Contact Details

Dr. Yinhao Li

You can also contact: doctoral.awards@ncl.ac.uk for independent advice on your application.

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