PhD in Electronic and Electrical Engineering - Resilient and Circular Power Electronic Inverters for Photovoltaic Systems in Modern Smart Grids

About the Project

Start date: 01 July 2026

Project background and aim

A fully funded PhD position for 3 years open to all international and home students to apply for.

This PhD programme is funded by the EU Horizon project “PiVot” in collaboration with about 16 partners within EU (including ABB, Siemens, and Heliox), and 9 industrial and research partners are from Netherlands, Italy, Romania, Finland, Cyprus, Portugal and the United Kingdom. PiVot develops innovative smart multi-level inverter solutions to enhance integration of Medium Voltage (MV) photovoltaic (PV) systems into the grid, addressing demands for flexibility, efficiency, and sustainability. Contributing to EU and the UK climate targets and neutrality, PiVot advances inverter technologies that enable renewable integration, improve grid stability, and support energy system digitalisation. Extending the single-phase PUC5 to three-phase medium-voltage systems, the project will validate a novel multilevel inverter topology for higher power density, efficiency and power quality.

The recruited PhD candidate will implement artificial intelligence and machine learning methods to analyse big sets of data and real-time measurements towards better understanding the behaviour of power inverters, optimising its design, and optimal control, together with the protection in terms of prognostic and diagnostic health management for both PV systems and their power converters. In this project, we will mainly discuss how AI techniques could help address the challenges in life cycle assessment and cybersecurity of power electronic converters in PV systems:

LCA

This PhD programme will evaluate the environmental performance of the proposed multilevel PV inverter across its entire lifecycle, from raw material extraction to end-of-life, ensuring alignment with EU sustainability and circular-economy targets. A comprehensive lifecycle assessment (LCA) methodology will be developed to quantify environmental impacts during material sourcing, manufacturing, operation, and recycling or disposal. Sustainability metrics from the LCA will strengthen the environmental value proposition and green market positioning. The tasks are as follows: (i) development of the LCA methodology and environmental impact analysis; (ii) integrate sustainability and circular-economy metrics into the framework; and (iii) contribute to the business case by defining green value proposition and environmental competitive advantage.

Cybersecurity

This project will explore cybersecurity for multilevel inverters following a structured, end-to-end methodology. Initial threat modelling and risk assessment will identify vulnerabilities in inverter control and inverter-to-grid communications. Intrusion and anomaly detection will combine rule-based methods with machine learning tailored to DER traffic and inverter control signals. Statistical analysis of network behaviour and operational logs—using supervised and unsupervised learning—will enable real-time detection of cyber-physical attacks. A platform will enable cybersecurity stress-testing through simulated intrusion attempts and data-breach scenarios, ensuring resilient and secure data exchange between inverters, storage systems, plant controllers, and grid operators. The tasks are as follows: (i) define the cybersecurity architecture; (ii) implement strong authentication, encryption, and secure data exchange; and (iii) develop and validate AI-based intrusion and anomaly detection.

Research Group and Supervision

The successful PhD candidate will experience cutting-edge research within a multidisciplinary team in “Propulsion, Electrification & Superconductivity” group and CryoElectric Research Lab, in which individuals from a diverse range of backgrounds are all supported to thrive. The PhD student will join at an exciting time for the group, following substantial external investment in artificial intelligence and machine learning capabilities for the electric power and superconducting applications for power network and aviation application that the PhD candidate’s research will integrate with. The group consists of 14 researchers (PGR/PhD students, research assistants, postdoctoral researcher, and academics) conducting leading research in applied superconductivity for large scale power applications. The successful PhD candidate will be offered internal collaboration opportunities to work with peers and therefore, contribute to other research as well. We collaborate with a range of academic and industrial partners within the UK and globally (Italy, Netherlands, France, China, Switzerland, New Zealand, and USA). There will be opportunities for the PhD student to be trained and to learn modelling techniques such as finite element modelling in COMSOL, equivalent circuit modelling in MATLAB/SIMULINK, and AI/ML surrogate modelling in Python.

The PhD candidate will be in close engagement with the industrial partners involved in this project and will exchange knowledge with them, offering a unique industrial slant to this research post. There will be opportunity for research visit and participating in international conference/workshop during this PhD to disseminate the findings and results of the research work.

More information about PI’s research can be found at:

https://www.gla.ac.uk/schools/engineering/staff/mohammadyazdaniasrami/

Research culture, support and student benefits

PhD students are part of a Graduate School that provides the highest level of support to its students and a number of benefits in addition to exceptional teaching and supervision, including:

• Research Training programme
• Diverse range of research-community activities
• Mobility scholarships
• Access to state-of-the-art facilities
• Opportunities for industry engagement

How to apply

Informal enquiries are welcomed and should be directed to Dr. Yazdani-Asrami - Email Address: Mohammad.yazdani-asrami@glasgow.ac.uk by sending a CV and a cover/intention letter. Please be to the point explaining how your previous experiences are well-aligned with this PhD position. He will then refer to the application process. Interview will be done on the rolling-basis during the advertisement period.

Eligibility criteria

• The project calls for a student with an enthusiasm for working in AI/ML for cyber security of power electronic systems, and photovoltaic/solar systems with real-world applications, coupled with strong programming skills.
• Applicants should hold a master’s level qualification, before start date.
• Excellent time and project management skills are essential.
• A good background in coding (MATLAB, Python, and/or C++) and AI/ML techniques would be essential.
• Prior knowledge of AI modelling for cybersecurity of power electronic in PV systems would be desirable but not essential.
• Having publications in reputable journals in the field of power electronics, PV, or cyber security is essential.
• The ideal student will have proven problem-solving abilities and an enthusiasm for innovation as well as creative and critical thinking.
• Excellent communication and interpersonal skills to integrate in the group is essential.
• Applicant must meet our English language entry requirement.

Funding Notes

This prestigious PhD position is fully funded. Funding will cover tuition fees for 3 years (36 months from the start date of the project), a stipend (about £21,000 per year – tax free), and a generous budget for running and training costs of research.