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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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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