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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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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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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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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
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analysis of complex, longitudinal, and high-dimensional data (e.g., immunometabolic profiles, clinical data, biomarkers). Development and application of predictive models and algorithms for diagnostics
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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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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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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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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into how algorithmic systems influence the circulation of information and disinformation across digital platforms, and how such processes affect perceptions of credibility, truth, and democratic