RSS Feed Source: Academic Keys

Research Opportunities

Electrical Engineering

Work description

Implementation of experimental test platforms in the x-Energy laboratory suitable for testing and validation of developments related to the H2Driven project, namely the hydrogen electrolyzer.

This includes the preparation of the power-hardware-in-the-loop setup, and associated electrical and automation works.

Academic Qualifications

Electrical Engineering.

Minimum profile required

Previous academic background in applied electrical engineering and automation.

Preference factors

Academic background or previous experience related to electrical installations, automation, or electronics. Knowledge of programming in Python, C, or C++.

Maintenance stipend: € 1040.98, according to the table of monthly maintenance stipend for FCT grants , paid via bank transfer. Grant holders may be awarded potential supplements, according to a quarterly evaluation process (Articles 19, 21 and 22 of the Regulations for Grants of INESC TEC and Annex II), up to a maximum limit of 50%

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RSS Feed Source: Academic Keys

Job ID: 257301

INESC TEC | Research Initiation Grant (AE2025-0208)
INESC TEC

Research Opportunities

Electrical Engineering

Work description

Study and identification of the main characteristics of INESC TEC’s optimal power flow tool, including all modeled energy resources. Study and modeling of two-stage stochastic programming and robust optimization problems. Adaptation of the tool formulation to stochastic and robust optimization. Implementation of the two-stage stochastic formulation in the existing tool, creating a specific module for its operation. Replication of the previous point for the formulation of the robust OPF. Prepare a scientific report on activities and write scientific articles.

Minimum profile required

Basic knowledge of the optimal power flow problem. Basic knowledge of optimization. Knowledge of the Python programming language. Fluency in English (written and spoken).

Preference factors

Experience

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RSS Feed Source: Academic Keys

Job ID: 257299

INESC TEC | Research Grant (AE2025-0224)
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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