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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust
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XFEL, ESRF, APS, and LCLS, depending on awarded beamtime. The project is supported by the Independent Research Fund Denmark. More information about the research group is available here: https
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department under the Faculty of Science & Technology at Aarhus University. Our work spans fields from physics, chemistry, microbiology, molecular biol-ogy, and mathematical modeling to social science
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Denmark. More information about the research group is available here: https://chem.au.dk/AmorphousMatLab What you will do The postdoc will co-lead beamtime proposals and campaigns, and will work with
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Aarhus University. Our work spans fields from physics, chemistry, microbiology, molecular biology, and mathematical modeling to social science, geography, economics, and policy analysis. Both basic and