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of machine learning frameworks Your research will include using models and codes to investigate the optimized design, integration, and intelligent operation of thermal energy storage systems in industrial
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framework across a suite of practically relevant optimization problems in public transport planning and airport operations. The broader project also includes research on heuristic methods, in particular Large
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in a team. You should ideally have: A master's degree in Engineering or Economics with good competences in operation research and optimization. Experience with energy system modeling - ideally of large
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dynamic community of PhD students and actively supports diversity. We are looking for a motivated applicant with good competences in operation research who wants to gain hands-on experience in cutting-edge
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some background in one or more of the following areas: Mathematical Optimization / Operations Research Reinforcement Learning, Machine Learning, and/or Multi-agent systems Game Theory Algorithms
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and operation of plasma devices—such as tokamaks and linear plasma systems—is also essential. In this role, you will lead plasma heating experimental research at DTU using RF and microwave technologies
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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will serve as a foundation, enabling future studies on novel quantum phenomena in these newly accessible quantum superlattices. Specific research activities will include: Developing and optimizing ultra
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skills and a keen interest in data-driven research. Your role will be to apply the developed semantic infrastructure to concrete case studies—such as cross-unit scheduling, process optimization
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with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal