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will generate synthetic data from personal data to mitigate scarcity in rare diseases. This will facilitate data availability for further development of predictive models, thereby enhancing the quality
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for efficient last-mile deliveries with a focus on climate and flexibility. Using advanced modeling and data analysis, you’ll create solutions that make final deliveries smarter and more sustainable. Your work
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today and in the future. For more information: http://www.slu.se/en/departments/forest-ecology-management/ Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about
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inhibitors, nanoparticles, and specific antibodies targeting these interfaces as treatments for infections. Within the research group, we use methods in molecular biology, cellular infection models
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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, where AI models are trained without having all data in a single computer. This makes it possible to use larger datasets for training, without sending sensitive data between hospitals. The goal is to
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well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive
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neurovascular biology and integrates advanced molecular analyses with experimental models and patient-derived material. The group is based at the Faculty of Medicine, Lund University, and operates in
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today and in the future. For more information: http://www.slu.se/en/departments/forest-ecology-management/ Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about
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group of Peter Swoboda, together with collaborators, is investigating ciliogenesis and cilia function in human neuronal cell lines and in vivo in a genetically tractable animal model, the worm C. elegans