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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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Job Description We are seeking a senior researcher with a strong scientific background, deep passion for innovation, proven leadership experience, and a collaborative mindset to lead a research
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multidisciplinary research in energy markets, optimization, game theory, and machine learning. Our team of 13 members (link ), from 10 different nationalities, values diversity and includes experts from a range of
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deep understanding of techno-economic evaluation and proven capabilities in the evaluation and modeling of resource recovery and valorization pathways. The role also requires experience in process and
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, finite element simulation tools, path planning and slicing tools, etc.). Deep enthusiasm for conducting original research in an interdisciplinary and international team and strive for scientific excellence
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Key Responsibilities (in close collaboration with TU Munich): Develop a deep understanding of materials engineering principles to guide the design and functionalization of nanoporous materials
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Job Description You will join a supportive and dynamic research team working at the intersection of machine learning and operations research. Your main task will be to design and implement ML
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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in
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decision-making. Collaborate with international partners and contribute to joint research activities. Teach and co-supervise students at different levels in courses and associated projects. Publish and
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results. The ability to work collaboratively in a team with an open-minded spirit, embracing both teaching and learning opportunities. A genuine interest in discussing physics and engaging in thoughtful