PhD Studentship: Building Trustworthy EV Charging—Confidential and Explainable AI for Security and Privacy Manchester Metropolitan University in United Kingdom

[Home tuition fees (£5,006 for 2025/26) covered for 3.5 years; international students must pay the difference. Standard UKRI stipend (£20,780 for 2025/26) for the duration of the award.]

Manchester Metropolitan University invites applications for a funded PhD studentship focused on building trustworthy electric vehicle (EV) charging systems using confidential and explainable AI for security and privacy. As EVs transform transportation, their charging infrastructure faces increasing cyber security and privacy challenges. Many public charging stations lack robust protections, exposing users to risks such as data misuse and cyber attacks. Existing AI-based security tools often operate as 'black boxes,' generating alerts without clear explanations, which undermines user trust and slows EV adoption.

This research project aims to develop a Confidential and Explainable AI framework for EV charging networks. You will investigate advanced techniques including privacy-preserving analytics, Trusted Execution Environments, federated learning, interpretable anomaly detection, and adaptive consent mechanisms. The goal is to secure sensitive data while providing transparent, actionable insights to both users and operators. The project will involve participatory design workshops, simulation-based testing, and opportunities to publish in leading journals.

As a doctoral student, you will gain expertise in cyber security, AI/ML, and human-centred design, working in state-of-the-art facilities and collaborating with industry partners and transport authorities. The research will not only shape the future of secure EV charging but also impact broader cyber-physical systems worldwide. You will join an inclusive doctoral community, benefit from advanced training, interdisciplinary collaboration, and professional development opportunities.

Project aims and objectives:

• Create a secure and transparent EV charging system that protects user data and explains system decisions clearly.
• Identify common security and privacy risks in EV charging networks.
• Develop AI tools to detect unusual activity and explain alerts in simple terms.
• Design privacy features that empower users with clear choices about their data usage.
• Collaborate with EV experts and users to design intuitive interfaces.
• Test the system to ensure it is secure, understandable, and trusted by users.

Funding: Both Home and International students are eligible to apply. Home tuition fees (£5,006 for 2025/26) are covered for the 3.5-year award. International students must pay the difference in tuition fees (Band 2 for 2025/26). The studentship includes a standard UKRI stipend (£20,780 for 2025/26) for the duration of the award.

Eligibility: Applicants must hold a first-class or upper second-class degree (or equivalent) and a Master’s degree in Computer Science, Electrical Engineering, Cybersecurity, or a related discipline. Strong programming skills (Python or similar) are essential. Candidates should have knowledge of AI/ML concepts and techniques, and an understanding of cybersecurity principles and/or privacy-preserving methods. International students must cover the difference in tuition fees.

Application process: Interested applicants should contact Dr Tooska Dargahi ([email protected]) for an informal discussion. To apply, complete the online application form for a full-time PhD in Computing & Digital Technology. Submit the Doctoral Project Applicant Form, CV, and covering letter via the University’s Admissions Portal, quoting reference SciEng-TD-2026-27-Electric Vehicle XAI. The application deadline is 9 March 2026, with an expected start date in October 2026.