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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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programme. Collaborating with external partners on the practical and administrative coordination of research projects. Supporting PhD and postdoc research presentations and exhibitions. Writing newsletters
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that yield valid statistical conclusions (inference) on causal effects when using machine learning algorithms and big datasets. The project is part of the research environment Stat4Reg (www.stat4reg.se ), and
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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of the project is to elucidate the fate, distribution, and degradation pathways of PFAS during thermochemical conversion of PFAS-containing biomass and waste feedstocks, and to assess their potential presence in
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electron microscopy (CLEM). BICU is part of a distributed National Microscopy Infrastructure (NMI):a Swedish infrastructure funded by the Swedish Research Council (VR-RFI) and cofinancing from
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Project Description The Department of Community Medicine and Rehabilitation is seeking a Postdoc for the
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principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several national and international research groups, edits one of the major
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organic pollutants are taken up, distributed, metabolized, and excreted in zebrafish. We have a particular interest in critical developmental stages, including early development and juvenile stages
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writing scientific papers and communicating our research advances in conferences. Methods: programming a humanoid platform using ROS2 packages, solve SLAM, use imitation learning algorithms to learn pick