AI-Driven Wind Farm Optimization Expert
6 days ago
DTU invites applications for a position as a PhD student 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.
You will have the opportunity to work with advanced training in various European universities and industry, 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).
This is an excellent opportunity to evolve your career within academia at DTU Wind, located in Risø, near Roskilde, just 30 minutes from Copenhagen.
We are seeking a passionate, curious, and enthusiastic new team member committed to addressing climate change by advancing renewable energy systems. We are looking for someone who thrives on solving complex problems, isn't afraid to ask challenging questions, and possesses the determination to pursue innovative solutions in a rapidly evolving field.
The primary tasks will be to develop and test new AI-driven algorithms for WFC, emphasizing accuracy, reliability, and robustness. You will also include impact assessment to estimate the potential benefits of the AI-driven WFC strategies compared to physics-based approaches.
A successful candidate should have a two-year master's degree 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 programming with scientific Python, data science/analytics, and machine learning methods, as well as wind energy topics.
We offer a rewarding and challenging job in an international environment with a competitive salary ranging between $60,000 - $80,000 per year, depending on qualifications and experience. The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations.
The period of employment is 3 years, and the starting date is flexible. Your complete online application must be submitted no later than September 15th, 2024. Applications must be submitted as one PDF file containing all materials to be given consideration.
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