Energy Systems – Distributed Artificial Intelligence and Edge Computing
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Work description
- Conduct a literature review and critically analyse the state of the art in Edge AI, distributed intelligence, edge computing, federated learning, and smart charging, with a particular focus on applications to energy systems and distributed energy resources. - Study the computational and functional requirements for executing algorithms on edge devices, taking into account limitations in processing power, memory, energy consumption, latency, connectivity, and robustness. - Develop and test electric vehicle charging session forecasting algorithms, including the prediction of start times, duration, required energy, and flexibility availability. - Explore the use of machine learning models and edge-optimized small language models, assessing their applicability in environments with limited computational resources. - Experimentally validate the developed solutions in representative scenarios, namely smart electric vehicle charging and flexibility management of distributed resources. - Critically analyse the results obtained and contribute to the preparation of technical documentation, reports, and scientific publications related to the work carried out.
Academic Qualifications
Bachelor’s or Master’s degree in Electrical and Computer Engineering, Informatics, Computer Science, Applied Mathematics, or related fields.
Minimum profile required
- Advanced knowledge of a programming language (e.g., Python, C++).- Basic knowledge of data analysis.
Preference factors
- Knowledge of electrical power systems, with a particular interest in distribution networks, distributed energy resources, or electric mobility. - Experience in software and API development, preferably using Python or equivalent programming languages. - Knowledge of artificial intelligence, machine learning, or federated learning. - Fluency in English (written and spoken).
Application Period
Since 08 Oct 2026 to 08 Nov 2026
[Open soon]
Centre
Power and Energy Systems