Artificial Intelligence

Work description

1) Hybridization of physics-based modelling and AI to create decision-support models for human operators under forecast uncertainty.

2) Distributed learning algorithms and coordination strategies to achieve a global goal where, for instance, aspects such as energy consumption (green AI) is covered by testing their implementation in edge devices.

3) Validate the developed methodologies on real data and different use cases focused on energy transition.

4) Dissemination of the work in international journals and/or conferences

Academic Qualifications

PhD degree in: Applied Mathematics; Physics; Computer Science; Electrical and Computer Engineering; Industrial Engineering or similar

Minimum profile required

- Past experience with supervised and reinforcement learning

- Python programming skills

- A minimum of 2 publications in Q1 journals

Preference factors

- Experience in applying reinforcement learning algorithms to engineering problems

- Knowledge or experience of energy systems problems

Application Period

Since 25 Jan 2024 to 24 Feb 2024

Cluster / Centre

Power and Energy / Power and Energy Systems

Scientific Advisor

Ricardo Jorge Bessa