Tasks will include: develop machine learning models with high interpretability and the ability for natural language processing; formulate data-based optimization problems (using supervised techniques and reinforcement learning); apply innovative data-drive techniques to use cases related to electricity markets and grid operation under emergency condition; dissemination of work in international journals.

The candidate is required to have a PhD degree in electrical engineering or similar.

The minimum requirements for the profile are a doctorate, and two publications in Magazines Q1 or Q2 of the area sustainable energy systems.

Preference will be given to candidates who have: Experience with machine learning models; Advanced knowledge in electrical power systems Knowledge in Python programming Knowledge of metaheuristics for optimization.

Scientific advisor: Ricardo Jorge Bessa.