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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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, 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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tasks will be to: Genetic engineering of bacteria. Phenotypic characterisation of engineered strains. Teach and supervise BSc and MSc student projects. You must have a two-year master's degree (120 ECTS
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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
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to acquire greater knowledge about basic scientific problems and to conduct research oriented towards use in societies and companies. Technology for people DTU develops technology for people. With our
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mindset, be curious and eager to learn. Since you will join an international group, a good command of written and spoken English is necessary. Your academic background should include a Master degree (or