Electrical Engineering - Electricity Markets
Work description
The increasing digitalisation and decentralisation of the energy system have driven the development of intelligent decision-support tools capable of integrating real-time data and optimising the operation of energy communities. In this context, Digital Twins are emerging as interactive digital platforms that replicate the behaviour of physical systems, enabling the simulation, forecasting, and optimisation of energy operations. The integration of Generative AI (GenAI) models and Explainable AI (XAI) methods is essential to ensure that the recommendations produced by these systems are not only effective but also transparent, interpretable, and trustworthy for different types of users. The main activities planned include: - Studying and reviewing the state of the art in Explainable AI applied to energy systems, Digital Twins, and generative models; - Developing and integrating XAI modules that explain, in an accessible manner and adapted to the user’s level of literacy, the recommendations generated by the system; - Implementing and fine-tuning generative models and LLMs, enhancing the system’s ability to generate automated responses and contextualised recommendations; - Designing interactive and intuitive visualisations, enabling the presentation of results and explanations with different levels of detail (from technical to non-specialised); - Testing and validating the system’s usability, robustness, and transparency, using real data and representative case studies; - Preparing technical documentation and scientific reports, as well as contributing to the dissemination of results through scientific papers and presentations.
Academic Qualifications
Electrotechnical Engineering or similar
Minimum profile required
- Basic knowledge of Artificial Intelligence and Machine Learning;- Fundamental understanding of Explainable AI (XAI) and/or Generative AI models (GenAI, LLMs);- Programming skills in Python, including the use of scientific and AI libraries (e.g., NumPy, Pandas, TensorFlow, PyTorch);- Fluency in English (written and spoken);- Strong analytical and problem-solving skills, with an orientation towards applied research work.
Preference factors
- Experience in Artificial Intelligence, with a focus on Explainable AI (XAI) and Machine Learning; - Knowledge and experience in Generative AI (GenAI) and Large Language Models (LLMs); - Experience in energy systems, particularly in modelling, optimisation, and energy community management; - Programming skills in Python and experience with AI frameworks (such as TensorFlow, PyTorch, or similar); - Experience in developing interactive interfaces and data visualisation tools.
Application Period
Since 16 Oct 2025 to 29 Oct 2025
Centre
Power and Energy Systems