PhD Scholarship in Wind Farm Control Optimization using Reinforcement Learning at DTU Wind
4 weeks ago
DTU Wind is seeking a highly motivated PhD student to work on a project focused on wind farm control optimization using reinforcement learning techniques. The project aims to develop and test new AI-driven algorithms for wind farm control, emphasizing accuracy, reliability, and robustness.
The successful candidate will have a strong background in engineering or computer science, with experience in programming and machine learning. They will be part of the IntelliWind Doctoral Network, which provides training for a new creative, entrepreneurial, innovative, and resilient industry-oriented academic generation.
The PhD student will work on developing and testing new AI-driven algorithms for wind farm control, including impact assessment to estimate the potential benefits of the AI-driven WFC strategies compared to physics-based approaches. They will also assess the risk and uncertainties through uncertainty assessment involving, for example, ensemble modeling.
The project will take advantage of collaboration with researchers and engineers at DTU and at external partners in academia as well as the industry. The PhD student will have the opportunity to visit industry and other academic institutions within the consortium.
The IntelliWind Doctoral Network provides training for a new creative, entrepreneurial, innovative, and resilient industry-oriented academic generation ready to face current and future challenges towards reducing the role of humans in the decision process and the need for direct human interventions in the operations and maintenance process.
The PhD student will be enrolled in one of the general degree programmes at DTU and will be subject to academic approval. The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations.
The position is a full-time position, and the starting date is 1 December 2024. The period of employment is 3 years.
The successful candidate will have a strong background in engineering or computer science, with experience in programming and machine learning. They will be part of the IntelliWind Doctoral Network, which provides training for a new creative, entrepreneurial, innovative, and resilient industry-oriented academic generation.
The PhD student will work on developing and testing new AI-driven algorithms for wind farm control, including impact assessment to estimate the potential benefits of the AI-driven WFC strategies compared to physics-based approaches. They will also assess the risk and uncertainties through uncertainty assessment involving, for example, ensemble modeling.
The project will take advantage of collaboration with researchers and engineers at DTU and at external partners in academia as well as the industry. The PhD student will have the opportunity to visit industry and other academic institutions within the consortium.
The IntelliWind Doctoral Network provides training for a new creative, entrepreneurial, innovative, and resilient industry-oriented academic generation ready to face current and future challenges towards reducing the role of humans in the decision process and the need for direct human interventions in the operations and maintenance process.
The PhD student will be enrolled in one of the general degree programmes at DTU and will be subject to academic approval. The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations.
The position is a full-time position, and the starting date is 1 December 2024. The period of employment is 3 years.
The successful candidate will have a strong background in engineering or computer science, with experience in programming and machine learning. They will be part of the IntelliWind Doctoral Network, which provides training for a new creative, entrepreneurial, innovative, and resilient industry-oriented academic generation.
The PhD student will work on developing and testing new AI-driven algorithms for wind farm control, including impact assessment to estimate the potential benefits of the AI-driven WFC strategies compared to physics-based approaches. They will also assess the risk and uncertainties through uncertainty assessment involving, for example, ensemble modeling.
The project will take advantage of collaboration with researchers and engineers at DTU and at external partners in academia as well as the industry. The PhD student will have the opportunity to visit industry and other academic institutions within the consortium.
The IntelliWind Doctoral Network provides training for a new creative, entrepreneurial, innovative, and resilient industry-oriented academic generation ready to face current and future challenges towards reducing the role of humans in the decision process and the need for direct human interventions in the operations and maintenance process.
The PhD student will be enrolled in one of the general degree programmes at DTU and will be subject to academic approval. The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations.
The position is a full-time position, and the starting date is 1 December 2024. The period of employment is 3 years.
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Kongens Lyngby, Lyngby-Tårbæk Kommune, Denmark Danmarks Tekniske Universitet (DTU) Full timePhD Scholarship in Wind Farm Control OptimizationWe are seeking a highly motivated and talented PhD student to join our team at DTU Wind in the field of wind farm control optimization. The successful candidate will have the opportunity to work on a cutting-edge project that focuses on developing and testing new AI-driven algorithms for wind farm control,...
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