JOB DESCRIPTION

As a postdoctoral researcher, you will contribute to the project GridData – Metrology for reliable power grid data analytics, a European metrology project aimed at enabling safe and reliable operation of electricity grids in an era of high renewable energy penetration and increasing system complexity.
Your role will focus on the validation and benchmarking of data analysis methods used for grid monitoring and forecasting, especially under scenarios with high uncertainty, abnormal phenomena, or incomplete data. You will collaborate with a multi-partner consortium across Europe to deliver a framework that quantifies data quality, integrates measurement uncertainty, and provides reference datasets based on real and simulated data.
By helping define and evaluate metrologically sound methodologies for assessing the performance and reliability of algorithms—including AI-based approaches—you will support the grid operators, measurement supply chains, and standards organisations in adopting data-driven decision support tools. Your contribution will ensure these tools meet the requirements of the EU AI Act for high-risk systems like energy infrastructure.
You will co-develop synthetic datasets using digital twins and support the application of the framework to real-time event detection and forecasting of grid congestion and imbalance. The work will involve close collaboration with both research institutes and industry stakeholders. Your results will contribute to new best practices and standardisation efforts within European committees.
You will work with Dr. Simon Tindemans and a team of researchers working on flexible, risk-aware energy systems. You will be a member of the Intelligent Electrical Power Grids (IEPG) Section of the Electrical Sustainable Energy (ESE) Department, which is part of the Electrical Engineering, Mathematics,and Computer Science (EEMCS) Faculty.

JOB REQUIREMENTS

• PhD in Electrical Engineering, Applied Mathematics, or a related field, with a focus on power systems, forecasting, or metrology.
• Demonstrated experience with probabilistic forecasting, grid congestion modeling, or data-driven decision support, particularly in operational environments.
• Proven experience working with real-world grid datasets, feature engineering, and uncertainty quantification in AI-based models.
• Knowledge of DSO processes and grid operator needs, ideally from prior applied research or industrial collaboration.
• Proficiency in Python or similar for model development, validation, and integration with hardware-in-the-loop testing environments.
• Strong scientific writing and communication skills in English.

Nice to haves:

• Familiarity with real-time digital simulators (RTDS), RSCAD, or similar platforms.
• Experience with validation frameworks, metrological uncertainty, or standardisation in a data science context.
• Prior collaboration with Dutch or European grid operators, or familiarity with energy policy and regulatory frameworks (e.g. EU AI Act).
• Proficiency in Dutch is a plus for stakeholder engagement.
• Active participation in open-source software, dataset publication, or reproducible research practices.

Please refer the Job description for details