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on tribological properties of dispersed and multi-component food systems. What will you do? The quality and sensory perception of foods is determined by the structure and physicochemical properties
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institute internationally recognised, focused on scientific excellence and societal impact, able to contribute through multi-disciplinary and intersectoral research, in an active and inclusive way to a
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Algorithms, (Byzantine) Fault Tolerance, Peer-to-Peer Networks. Online Algorithms: Partially-Observable Markov Decision Processes (POMDPs), Cooperative and Competitive Multi-Agent Reinforcement Learning (MARL
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are particularly difficult to treat, with poor treatment outcomes and high relapse rates. However, current treatments are moderately effective at best. Given available therapies are designed to target known core
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innovative approaches to mitigate and prevent corrosion. You will contribute to the development of autonomous workflows for studying corrosion. These implementations will be general and will be applied to core
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), Umeå University (1), University of Tübingen(1), Uppsala University (1), Human Technopole (1), The University of Tromsø – The Arctic University of Norway (1), Biological Research Centre of the Hungarian
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on Snow and ice parameter estimation using high frequency multi-modal SAR. Job description The research focuses on the retrieval of snow and ice parameters and their dynamics using multimodal Synthetic
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mechanics at the atomic scale. In this project, the University of Groningen will develop an array of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant
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Media (EDM), the Research Training Group KD²School (KD²School), π³: Parameter Identification – Analysis, Algorithms, Implementations (RTG π³), the UBRA AI Center for Health Care , and the ZeMKI
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of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant for the new green steels compositions, including impurities and tramp elements. These models should enable