Artificial Intelligence (AI)

PhD Studentship in Computer Science: Empirical Security Assessment of AI Decision Engines in Cyber-Physical Systems

Newcastle University View all jobs

• Newcastle


• £21,805 per year


• Contract


• Full-time

22 hours ago

Award summary

100% home fees covered, and a minimum tax-free annual living allowance of £21,805 (2026-27 UKRI rates).

Overview

Artificial Intelligence (AI)-driven decision engines are increasingly embedded into cyber-physical systems (CPS) such as autonomous vehicles, smart grids, industrial control systems, robotics, healthcare devices, and intelligent transport infrastructure. While significant research has focused on improving the performance, efficiency, and autonomy of such systems, their empirical security assessment remains underdeveloped, representing a growing and critical gap.

Recent studies have highlighted vulnerabilities in learning-enabled CPS, demonstrating that small, carefully crafted perturbations can cause unsafe or malicious behaviours. However, much of the literature focuses either on theoretical attack construction or single-system evaluations, rather than on systematic, reproducible, and comparative empirical assessments across classes of AI decision engines. This PhD project aims to address this gap through the development of a rigorous empirical framework for evaluating the security robustness, failure modes, and operational risks of AI decision engines in cyber-physical environments.

Impact and Relevance

The proposed research directly addresses challenges faced by industries deploying AI-enabled CPS in safety-critical environments such as transportation, energy, and manufacturing. By providing empirical evidence rather than purely theoretical assurances, the project will support regulators, system designers, and security engineers in making informed deployment decisions. The outcomes are expected to influence both academic research and industrial best practices for trustworthy AI in cyber-physical systems.

• Number of awards: 1


• Start date: 21st September 2026


• Award duration: 3.5 years


• Sponsor:


• Supervisors

Eligibility criteria

• You must have, or expect to gain, a minimum 2:1 Honours degree or international equivalent in a relevant subject or subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently, excellent analytical skills and strong verbal and written communication skills are also essential requirements.


• The studentship covers fees at the Home rate (UK and EU applicants with pre-settled/settled status and meet the residency criteria). International applicants are welcome but must cover the difference between Home and International fees.


• Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills.


• International applicants may require an ATAS ( ) clearance certificate prior to obtaining their visa and to study on this programme.

How to apply

You must apply through the University's

#s1-Gen

You must have, or expect to gain, a minimum 2:1 Honours degree or international equivalent in a relevant subject or subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently, excellent analytical skills and strong verbal and written communication skills are also essential requirements.
The studentship covers fees at the Home rate (UK and EU applicants with pre-settled/settled status and meet the residency criteria). International applicants are welcome but must cover the difference between Home and International fees.
Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills.
International applicants may require an ATAS ( ) clearance certificate prior to obtaining their visa and to study on this programme.

Artificial Intelligence (AI)-driven decision engines are increasingly embedded into cyber-physical systems (CPS) such as autonomous vehicles, smart grids, industrial control systems, robotics, healthcare devices, and intelligent transport infrastructure. While significant research has focused on improving the performance, efficiency, and autonomy of such systems, their empirical security assessment remains underdeveloped, representing a growing and critical gap.
Recent studies have highlighted vulnerabilities in learning-enabled CPS, demonstrating that small, carefully crafted perturbations can cause unsafe or malicious behaviours. However, much of the literature focuses either on theoretical attack construction or single-system evaluations, rather than on systematic, reproducible, and comparative empirical assessments across classes of AI decision engines. This PhD project aims to address this gap through the development of a rigorous empirical framework for evaluating the security robustness, failure modes, and operational risks of AI decision engines in cyber-physical environments.
Impact and Relevance
The proposed research directly addresses challenges faced by industries deploying AI-enabled CPS in safety-critical environments such as transportation, energy, and manufacturing. By providing empirical evidence rather than purely theoretical assurances, the project will support regulators, system designers, and security engineers in making informed deployment decisions. The outcomes are expected to influence both academic research and industrial best practices for trustworthy AI in cyber-physical systems.

100% home fees covered, and a minimum tax-free annual living allowance of £21,805 (2026-27 UKRI rates).
Overview