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development, networking, administrative and technical support functions, along with good employment conditions. More information about the department is available at: https://www.umu.se/en/department
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transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging
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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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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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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