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Field
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mathematics and physics-enhanced machine learning . Requirements Master in Informatics, Mathematics, or a related field (e.g. Computational Science/Engineering) Strong knowledge of machine learning, Scientific
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research the dynamic processes of the Earth system. Our vision: “Taking the pulse of the Earth to safeguard a habitable planet.” For section 4.4 "Hydrology" (department “Geosystems”), we are looking for a
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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topics of education, culture, management/technology, sustainability and state. Its international study model with the Leuphana College, the Leuphana Graduate School and the Leuphana Professional School is
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manufacturing in the context of future production, material, and energy systems Research on the state of the art in additive manufacturing processes with a focus on costs, material usage, and energy requirements
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and Manufacturing Processes (IMD-2), we work in a dynamic and international team of over one hundred materials scientists, chemists, physicists, mechanical engineers and technical staff
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simulations. Your Profile Excellent completed master's degree in physics, materials science, engineering or a related field. Good knowledge of solid state physics. Ideally, experience in the fields
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problem. Do a theoretical analysis of the assigned problem using tools from information theory and related fields. Design a solution which is implementable on a computer and in hardware. Collaborate with
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fields. Design a solution which is implementable on a computer and in hardware. Collaborate with colleagues to implement the solution in hardware at the ACES lab at the Institute of Theoretical
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Leibniz Institute of Ecological Urban and Regional Development (IOER) • | Dresden, Sachsen | Germany | about 6 hours ago
the Leibniz Institute of Ecological Urban and Regional Development (IOER) and Dresden University of Technology (TUD). Doctoral fellows are admitted through a competitive process to a relevant TUD faculty with