Electrical engineering - Power Systems
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Work description
Development of an AI-driven Energy Management System (EMS) for AC, DC, and Hybrid shipboard microgrid architectures, focused on optimal power dispatch and source-load coordination. The work involves implementing intelligent algorithms for real-time power arbitration and energy scheduling to balance power flow from diverse sources with fluctuating loads. Validation considering real vessel use cases in a laboratory environment. Publication of results in leading conferences and journals in the field. Specifically, the main activities to be carried out by the scholarship holder are: ? State-of-the-art analysis of AI-driven EMS for small-to-medium vessels, focusing on hybrid battery/fuel cell system integration. ? Requirement specification for the EMS layer, aligning maritime mission profiles with energy storage preservation and real-time efficiency. ? Development of energy models and efficiency maps to enable EMS trade-off analysis between power output, battery SoC, and system efficiency. ? Design of intelligent agents for automated energy dispatch, predictive maintenance-aware control, and operational monitoring. ? Implementation of coordination mechanisms between the EMS and power hardware to ensure smooth transitions between energy sources. ? Validation and testing using laboratory environment and realistic vessel scenarios to compare AI-based approaches against traditional heuristics. ? Scientific dissemination through the drafting of technical reports and high-impact journal articles to document algorithms and results.
Academic Qualifications
? Master’s degree in Electrical Engineering or a related field;
Minimum profile required
? Master’s degree in Electrical Engineering or a related field;? Applicants must either already be enrolled in a PhD programme or be available to enroll in a PhD programme at the earliest possible date;? Average grade in bachelor's and master's degrees higher than 14.? Experience in C or Python programming.
Preference factors
? Previous experience in Artificial Intelligence and Machine Learning applied to energy systems or microgrids. ? Previous experience in MATLAB/Simulink and power system modelling. ? Familiarity with AC/DC hybrid microgrid topologies and power electronics. ? Experience with real-time simulators (e.g., OPAL-RT).
Application Period
Since 01 May 2026 to 31 May 2026
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Centre
Power and Energy Systems