This fully funded PhD position at the University of Glasgow offers an exciting opportunity to contribute to the EU Horizon project “PiVot,” which is advancing innovative smart multi-level inverter solutions for integrating Medium Voltage (MV) photovoltaic (PV) systems into modern smart grids. The project is a collaboration with 16 EU partners, including industry leaders such as ABB, Siemens, and Heliox, and spans research and industrial partners across the Netherlands, Italy, Romania, Finland, Cyprus, Portugal, and the United Kingdom.

The PhD research focuses on developing resilient and circular power electronic inverters, addressing the demands for flexibility, efficiency, and sustainability in renewable energy integration. The candidate will implement artificial intelligence (AI) and machine learning (ML) methods to analyze large datasets and real-time measurements, optimizing inverter design and control, and enhancing prognostic and diagnostic health management for PV systems and their power converters.

Key research areas include:

• Life Cycle Assessment (LCA): Evaluating the environmental performance of multilevel PV inverters across their entire lifecycle, from raw material extraction to end-of-life. The project will develop a comprehensive LCA methodology, integrate sustainability and circular-economy metrics, and contribute to the business case by defining green value propositions and environmental competitive advantages.
• Cybersecurity: Exploring cybersecurity for multilevel inverters using structured methodologies. Tasks include threat modeling, risk assessment, intrusion and anomaly detection (combining rule-based and ML methods), statistical analysis of network behavior, and real-time detection of cyber-physical attacks. The project will develop and validate AI-based intrusion and anomaly detection, implement strong authentication and encryption, and enable cybersecurity stress-testing.

The successful candidate will join the “Propulsion, Electrification & Superconductivity” group and CryoElectric Research Lab, a multidisciplinary team conducting leading research in applied superconductivity for large-scale power applications. The group offers a supportive environment, opportunities for internal collaboration, and access to state-of-the-art facilities. Training will be provided in modelling techniques such as finite element modelling (COMSOL), equivalent circuit modelling (MATLAB/SIMULINK), and AI/ML surrogate modelling (Python).

Engagement with industrial partners is a key feature, providing a unique industrial perspective and opportunities for research visits and participation in international conferences and workshops. The PhD student will benefit from the Graduate School’s research training programme, mobility scholarships, and diverse research-community activities.

Funding: The position is fully funded for 3 years, covering tuition fees, a tax-free stipend (~£21,000/year), and a generous research/training budget.

Eligibility: Applicants must hold a master’s level qualification before the start date, have strong programming skills (MATLAB, Python, and/or C++), and experience with AI/ML techniques. Prior knowledge of AI modelling for cybersecurity of power electronic systems in PV systems is desirable but not essential. Publications in reputable journals in power electronics, PV, or cybersecurity are required. English language entry requirements must be met.

Application: Informal enquiries are welcomed. Send a CV and cover/intention letter to Dr. Yazdani-Asrami ([email protected]), explaining how your experience aligns with the position. Interviews are conducted on a rolling basis during the advertisement period. More information about the PI’s research is available at this link.

Deadline for applications: 10 May 2026.