UKRI

The University of Salford's School of Science, Engineering and Environment is offering a fully funded PhD scholarship focused on the detection and mitigation of False Data Injection (FDI) attacks in hydrogen-integrated microgrids. This project leverages digital twins and physics-informed AI-driven control to address the growing cyber-physical threats in modern, digitally enabled power systems. Hydrogen-integrated microgrids combine renewable generation (such as photovoltaics), short-term battery storage, and long-term hydrogen/fuel cell storage, alongside flexible loads like electric vehicle charging. While digitalisation enables advanced monitoring and optimisation, it also increases vulnerability to cyberattacks, particularly FDI attacks that can disrupt energy management systems and cause physical damage to critical infrastructure.

The research challenge is to develop a unified, resilient framework for real-time anomaly detection and system stability under active attack conditions. The project sits at the intersection of Power Systems, Cybersecurity, Control Engineering, and Physics-Informed Artificial Intelligence (PI-AI). The successful candidate will engage in four structured work packages: (1) enhancing a digital twin for cyber-resilience testing and AI dataset generation; (2) developing hybrid PI-AI algorithms that embed engineering principles into data-driven models for robust anomaly detection; (3) designing resilient Model Predictive Control (MPC) strategies to maintain system stability despite compromised data; and (4) validating the integrated framework through hardware-in-the-loop testing and demonstrator trials using the ESTIA microgrid platform and Salford’s smart energy research facilities.

Supervision is provided by Dr Arunachalam Sundaram, Dr Amir Nourian, Dr Adriana Aguilera Gonzalez, and Dr Ignacio Hernando Gil, with academic profiles available for further information. The scholarship is open to UK and international applicants and covers tuition fees, a UKRI stipend for 3.5 years, and a generous equipment/consumables budget. Applicants should have a background in Electrical Power Engineering, Energy Systems, or Control Engineering, though graduates from Mechanical or Chemical Engineering with an interest in interdisciplinary energy research are also encouraged. Analytical and programming skills are required, with familiarity or willingness to learn MATLAB/Simulink, Python, or Modelica/FMU. Interest in machine learning, digital twins, and cybersecurity is desirable but not mandatory. Strong scientific writing skills are expected.

To apply, submit a CV and personal statement (max 500 words) outlining your suitability, and an academic review (max 3000 words) with references demonstrating your knowledge of the field. Upload the academic review in the 'research proposal' section of the application dashboard. No separate research proposal is required, and generative AI must not be used in application materials. For further guidance, consult the official University of Salford application instructions. The application deadline is May 1, 2026.