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motivated PhD students, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service
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energy efficiency while keeping the grid reliable and secure. Our research method is engineering-oriented, prototype-driven, and highly interdisciplinary. Our typical research process includes
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, methods, and algorithms into existing high-performance frameworks, the fast prototyping of new ideas in individual code, an interest in the entire simulation pipeline: starting from simple algorithms
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mobility systems through practical and laboratory tests as well as sophisticated simulations. We not only publish research results gained at numerous conferences and in journals, but also make our software
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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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of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100% for PostDocs; 45k
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for their use as sensors or improving signal acquisition by modern image processing based on artificial intelligence. Magnetic imaging and near-field microscopy. We operate a scanning probe microscope that can
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investigate methods that eventually will automate crucial design steps. In addition, we are developing simulators (on various abstraction levels; using, e.g., Computational Fluid Dynamics) which enables us to