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at tunable temperature and photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning, and theoretical
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science, e.g, by leading to more effective batteries. The Research Assistant/Associate will join the Machine Learning Group at the Department of Engineering, working with Prof. José Miguel Hernández Lobato
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science, e.g, by leading to more effective batteries. The Research Assistant/Associate will join the Machine Learning Group at the Department of Engineering, working with Prof. José Miguel Hernández Lobato
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problems in biology by combining machine learning with in-depth knowledge of biological processes. Who we are looking for You have a Master in Science (Bioengineering, Biochemistry-Biotechnology, Biomedical
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problems in biology by combining machine learning with in-depth knowledge of biological processes. Who we are looking for You have a Master in Science (Bioengineering, Biochemistry-Biotechnology, Biomedical
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 15 hours ago
, Applied Mathematics, Physics, Biophysics or a similar qualification Interest in and/or experience with AI/machine learning A willingness to learn and extend this knowledge and master new challenges in
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systems”, coordinated by Prof. Dr. Marco Salvalaglio and Prof. Dr. Axel Voigt and funded by the German Research Foundation (DFG). The core activities will focus on the investigation of disordered correlated
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[#28618, IV-459/24, IV-460/24] Position Title: Position Type: Student programs Position Location: Berlin, Berlin, Germany [map ] Subject Areas: Applied Physics Biomedical Engineering AI/Machine Learning
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and