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Research Opportunities

COMPUTER SCIENCE – 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

Minimum profile required

Previous academic background in applied mathematics or computer science or informatics or electrical engineering or similar.

Preference factors

Past experience (or academic background) with supervised and reinforcement learning. Academic background in operations research. Programming knowledge in Python.

Maintenance stipend: € 1040.98 or 1309.64, according to the table of monthly maintenance stipend for FCT grants ,

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RSS feed source: US Energy Information Administration

Research Opportunities

Electrical Engineering – Electricity Markets

Work description

The use of the coalition concept is growing with the need to allow small consumers to participate in electricity markets that are designed for larger consumers and retailers. Through a coalition, small consumers are able to participate in wholesale markets for the purchase and sale of energy (when they have their own generation), and even participate in flexibility markets. However, organizing these coalitions is not a trivial task, and a management entity, for example, an aggregator, is traditionally needed to determine the entities that will be part of each coalition.

The expected work in this area concerning the description given is as follows:

Study in detail the existing models in the game theory literature for the study of modeling dynamic coalitions. Formulate the energy and flexibility transaction problem using game theory with dynamic coalitions.

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Job ID: 257302

INESC TEC | Research Grant (AE2025-0225)
INESC TEC

Research Opportunities

Computer Science

Work description

Development of model/process chains that enable AI-based assistants to support human operators’ decisions in power systems under model risk and uncertainty, and considering joint human-AI learning. Develop methodologies to assess the robustness and safety of human decisions assisted by AI, hybrid co-learning between AI and humans, and fully autonomous AI, considering risk assessment aligned with the EU AI Act, as well as reliability and robustness quantified through the provision of guidelines on how to create and use “adversarial” datasets. Validate the developed methodologies using real data and simulators. Dissemination of the work in national and/or international journals and conferences.

Academic Qualifications

Degree in Computer Science, Informatics of similar area.

Minimum profile

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