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. The aim of our group is to improve the understanding of the trade-offs between production, mitigation and conservation in livestock-based systems, and to identify innovative mechanisms for landscape-level
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). Employment Conditions • Start date: Flexible, from January 2026 onward • Salary: Based on the German public sector pay scale TV-L E13, commensurate with experience and qualifications. • Generous funding
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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, and high-performance computing. It aims to improve the performance of the matrix-free finite-element-based framework HyTeG, in particular by techniques for data reduction through surrogate operators
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the understanding of the trade-offs between production, mitigation and conservation in livestock-based systems, and to identify innovative mechanisms for landscape-level management. Our group combines empirical work
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01.07.2025, Wissenschaftliches Personal The position is based within the research group of Deniz Kus, Professor for Representation Theory at the Department of Mathematics, part of the TUM School
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. The position is based within the research group of Deniz Kus, Professor for Representation Theory at the Department of Mathematics, part of the TUM School of Computation, Information and Technology (CIT
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
control of such systems, taking particularly into account model uncertainties as well as limitations pertaining to acquisition of data, communication, and computation. We apply our methods mainly to human
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materials science • Extensive knowledge of computer-based modelling and simulation methods in materials science of metals, e. g. Calphad method, precipitation simulation, cellular automata, kinetic Monte
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News Detection Topic / Area: The positions are flexible in terms of research direction within 3D vision with a heavy focus on cutting-edge deep learning-based techniques. We are particularly interested