"PhD Studentship: Building Trustworthy EV Charging: Confidential and Explainable AI for Security and Privacy"

PhD Studentship: Building Trustworthy EV Charging: Confidential and Explainable AI for Security and Privacy

Manchester Metropolitan University

Qualification Type:PhDLocation:ManchesterFunding for:UK Students, International StudentsFunding amount:£20,780 stipend for the academic year 2025/26. Please see advert for further details.Hours:Full Time

Placed On: 5th December 2025

Closes: 2nd March 2026

Reference: SciEng-TD-2026-27-Electric Vehicle XAI

Project advert

Electric vehicles (EVs) are revolutionising transport, but their charging infrastructure faces growing cyber security and privacy risks. Many public chargers lack robust protections, leaving users vulnerable to data misuse and cyber attacks. Current AI-based security tools often act as “black boxes,” generating alerts without explanation, undermining users' trust and slowing adoption.

This PhD project will develop a Confidential and Explainable AI framework for EV charging networks. You will explore cutting-edge techniques such as privacy-preserving analytics, Trusted Execution Environments, and federated learning, combined with interpretable anomaly detection and adaptive consent mechanisms. The aim is to keep sensitive data secure while providing clear, actionable insights to users and operators.

You will gain expertise in cyber security, AI/ML, and human-centred design, working in state-of-the-art facilities and collaborating with industry and transport authorities. The project includes participatory design workshops, simulation-based testing, and opportunities to publish in leading journals.

Join our inclusive doctoral community and benefit from advanced training, interdisciplinary collaboration, and professional development. Your research will not only shape the future of secure EV charging but also influence broader cyber-physical systems worldwide.

Project aims and objectives

Aim:

To create a secure and transparent system for electric vehicle (EV) charging that protects user data, explains system decisions clearly, and builds trust between drivers and charging providers.

Objectives:

• Identify common security and privacy risks in EV charging networks.
• Develop AI tools that can detect unusual activity and explain why alerts occur in simple terms.
• Design privacy features that give users clear choices about how their data is used.
• Work with EV experts and users to design easy-to-understand interfaces.
• Test the system to ensure it is secure, understandable, and trusted by users.

Funding

Both Home and International students can apply. Only home tuition fees will be covered for the duration of the 3.5-year award, which is £5,006 for the year 2025/26. Eligible international students will need to make up the difference in tuition fee funding (Band 2 for the year 2025/26).

The student will receive a standard stipend payment for the duration of the award. These payments are set at a level determined by the UKRI, currently £20,780 for the academic year 2025/26.

Specific requirements of the candidate

• 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).
• Knowledge of AI/ML concepts and techniques.
• Understanding of cybersecurity principles and/or privacy-preserving methods.

How to apply

Interested applicants should contact Dr Tooska Dargahi (t.dargahi@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full time PhD in Computing & Digital Technology.

Please complete the Doctoral Project Applicant Form, and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.

Please upload these documents in the supporting documents section of the University’s Admissions Portal.

Applications closing date: 2 March 2026

Expected start date: October 2026

Please quote the reference: SciEng-TD-2026-27-Electric Vehicle XAI