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PhD Scholar in Reinforcement Learning for Wind Energy
1 week ago
We are inviting applications for a PhD student position in Wind Farm Control (WFC) Optimization, focusing on Reinforcement Learning (RL) techniques, as part of Design and impact assessment of autonomous actions investigated in IntelliWind. This project offers an exciting opportunity to contribute to the development of novel wind farm control strategies using AI-driven methods.
Your tasks will involve developing and testing new RL-based algorithms for WFC, including impact assessment to estimate the potential benefits of the AI-driven WFC strategies compared to physics-based approaches. Additionally, you will assess the risk and uncertainties through uncertainty assessment involving ensemble modeling.
This project provides a unique opportunity to collaborate with researchers and engineers at DTU and external partners in academia and industry. As a member of the IntelliWind Doctoral Network, you will participate in scientific discussions within the rich context of the Marie Skłodowska-Curie Action Doctoral Network (MSCA DN) "Intelligent systems for autonomous Wind power plant operations" (IntelliWind).
You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. In addition, we expect that you have some level of experience in programming with scientific python, data science/analytics and machine learning methods, and wind energy topics.
Dalborg campus is located 30 minutes from Copenhagen and offers a vibrant international environment. The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union.
