Trustworthy AI-Driven Digital Twins for Cyber-Resilient Connected and Autonomous Systems
Not stated
- Funding
- Self-Funded PhD Students Only
- Application deadline
- Year-round applications
About the project
About the Project Connected and autonomous systems increasingly rely on software-defined functions, sensor fusion, AI-enabled decision-making, V2X communications and cloud-edge coordination. These systems are difficult to secure because cyber risks may emerge across software, communication, perception, data and operational layers. This PhD will investigate how trustworthy AI-driven digital twins can support cyber-security testing, threat modelling, anomaly detection and resilience evaluation for connected and autonomous systems, including CAV/V2X, IoT/CPS and intelligent transport infrastructure. The project will develop methods for modelling cyber-physical attack surfaces, generating realistic attack scenarios, evaluating AI-based detection and response mechanisms, and measuring system resilience under adversarial conditions. Research may include digital-twin modelling, cyber-effects simulation, secure data pipelines, AI-based intrusion detection, V2X communication security, and resilience metrics. The project is expected to produce both theoretical contributions and practical demonstrators relevant to cyber-security assurance, safety-critical infrastructure and trustworthy digital transformation. The successful candidate will ideally have a background in cyber security, computer science, AI, communications, digital twins, software engineering or cyber-physical systems. Experience with Python, simulation environments, machine learning, networking or security testing would be beneficial.