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Field
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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in several of the following key topics: Energy System Technologies Digital Twin Platforms and Data Analytics Predictive Models & Explainable AI (XAI) Methods in Time Series Forecasting Data Retrieval
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a wealth of state-of-the-art methods, providing them with up-to-date concepts of modern biology and medicine. The combination scientific expertise and modern scientific facilities establishes a basis
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Master's degree in Energy Policy, Energy Economics, Information Systems, Mathematics, Operations Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in
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Systems, Economics, Mathematics, Operations Research, Computer Science, or a related field Strong knowledge of quantitative and/or computational research methods, ideally in optimization and simulation
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Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in numerical optimization and simulation models. Proficiency in one of the major programming
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use literary sociology and heritage studies methods to generate new knowledge on the development of a nationalist modernist canon in contemporary Europe. The student’s case study will complement Dr
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theoretical foundations and formal methods, and focuses on applications in the field of artificial intelligence and data science. There are many collaborations between the two institutes. For more information
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be formally positioned at the Department of Design, but also have access to a co-located working space for all NOTRAP PhD students at the Department of Psychology. The NOTRAP project will be organized