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are offering a fully funded PhD position in the field of Physics-informed Learning-based Control. This interdisciplinary research area bridges control theory, machine learning, and physics-based modeling
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to judge the applicant’s contribution for publications with multiple authors, a short description of the applicant’s contribution must be included. Additional relevant documentation of knowledge and skills
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, Robotics, Computer Science, Statistics, or related discipline. Strong background in Machine Learning and Control Theory. Demonstrated experience in research projects with industrial partners. Excellent
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, making use of economic theory and state of the art econometric methods. The Department of Economics has a strong research-oriented faculty working within microeconomic theory, macroeconomics, econometrics
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, making use of economic theory and state of the art econometric methods. The Department of Economics has a strong research-oriented faculty working within microeconomic theory, macroeconomics, econometrics
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works). If it is difficult to judge the applicant’s contribution for publications with multiple authors, a short description of the applicant’s contribution must be included. About The Faculty
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regulation section 5-2 (3) . Names and contact details for two references Additional relevant documentation of professional knowledge (for example, list of scientific works). If it is difficult to judge
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to contribute to foundational research at the intersection of control theory, optimization, and artificial intelligence, with potential applications in energy systems, and infrastructure networks. The successful
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to enable intelligent, real-time decision-making in large-scale interconnected systems. This position offers the opportunity to contribute to foundational research at the intersection of control theory
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. Project description (max 6 pages), including a summary and references, using this template . Please include area of research, research problems/questions, methods and use of theory CV (max 3 pages). Use