Challenge: Transforming the conventional carbon-intensive energy use
Change: AI to turn data into knowledge for efficient systems
Impact: Boosting the sustainable, fair and reliable energy transition
IMPORTANT. This PhD can also be completed in Dutch.
The Data Science group is part of the Institute for Computing and Information Sciences at the Radboud University Nijmegen. We develop theory and methods for machine learning and apply them in various fields. As energy systems are becoming more complex, machine learning and AI open new possibilities to efficiently manage them and help building more sustainable and reliable systems.
Alliander is a Distribution system operator there to maintain, operate the medium-low voltage grid and connect end-users with generators to the transmission high-voltage grid. Alliander is one of the frontrunners worldwide as a DSO in applying smart algorithms and AI, it is also one of the places where the limits of existing methods and available commercial software surface.
Together, we combine ground-breaking Artificial Intelligence (AI) methods with the reliable theory of the physical energy system. The area of data-driven scientific computing promises to combine statistics, time-frequency analysis, low-dimensional model reductions, and other techniques to extract information from data. With AI, we make such information useful for the management and planning of complex energy systems. For example, it is possible to use neural networks to model the state of the grid so operators can act in full awareness of the operating situation. We investigate explainable AI and data-driven scientific modellings such as reinforcement learning, graph neural networks for their applicability for state estimation, decentralised control of energy flows, risk-based investments and operation. You will work as a PhD researcher and will be supported by around 10 Scientific staff and data scientists and power system experts at Alliander. You will integrate your own ambitious research program within our research vision.
This PhD position focuses on developing and applying machine learning methods, in particular graph neural networks (GNNs), to electricity and gas network data generated within Alliander. Various applied problems within Alliander will be tackled using this class of methods, for example, resilience assessment of distribution grids and finding the best place for intelligent components, circuit breakers, and grid openings to minimize the annual total outage duration. From a methodological point of view, emphasis will be on uncertainty quantification for GNNs as well as domain adaptation for generalization of GNNs to unseen grids.
- The PhDs of this project will be employees at the knowledge institutes
- The submitted documents should include: CV, motivation letter, grades of all higher education degree programs (BSc, MSc, …), MSc thesis or (if not yet completed) another sample of academic/technical writing
- Expiry date of the applications 31 August 2022
An MSc degree in computer science, AI, data science, mathematics, electrical engineering or a related field.
Proficient in programming languages used in scientific computing, such as, Python and a strong willingness to develop programming skills further.
A strong background in statistics/machine learning, experience with deep learning is a plus.
Fluency in verbal and written English.
Excellent communication, presentation and writing skills.
Comfortable in working in multi-disciplinary team.
Alliander screens all applicants. Depending on the position, the screening consists of the following steps: checking references, checking the authenticity of identity papers and diplomas, an integrity check and requesting a certificate of conduct (VOG).
Interested? Apply here!
You're able to apply by clicking on the following link: https://www.ru.nl/en/working-at/job-opportunities/phd-candidate-graph-neural-networks-for-electricity-and-gas-networks
Prof. Tom Heskes email@example.com