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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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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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, electrophysiology, chemo-optogenetics. Behavioral measurements will be performed in collaboration with Olof Lagerlöf’s group. The postdoc will image (and influence with opto-chemogenetics) the network representation
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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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Group” (web: opeg-umu.com ), and the selected postdoc will be part of a larger collaboration, with industry and international academic actors, aimed at the development of a printed white-emitting LEC