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Field
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particular attention to how AI systems reshape organizational processes, decision-making practices, and service delivery. The project is situated within Swedish Center for Digital Innovation at the Department
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-dimensional Bayesian inverse problems for image reconstruction and chemical reaction neural networks with sparsity-promoting (and edge-preserving) priors, including diffusion-based approaches. Neural solvers
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requirement. A very good command of the English language, both written and spoken, is a key requirement. Experience in Federated Learning, Computer Vision, Image Analysis, Mathematics, and Mathematical
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conventional harvesting. The research will involve practical DNA sampling, high-throughput genotyping, and data fusion using machine-generated harvest data, annotated images, and environmental information
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complex systems. Development and application of theoretical tools that combine experimental data and atomistic computer simulations to provide a comprehensive picture that is difficult to achieve through
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its dependency on the pre-fire forest structure and management practices in Fennoscandian boreal forests. Work includes e.g. developing methods to assess burn severity and fuel structure, processing
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fuel structure, processing of different remote sensing and mapped data, statistical modelling, mapping of fire risk and analyzing the effect of forest management for fire behavior. Considered remote
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design using advanced CMOS technologies, along with a solid background in analog and digital signal processing systems. Comprehensive understanding of the entire IC design flow, including circuit design
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, diagnosis, drug response and health monitoring. Research in precision medicine is expected to use existing strong assets in Sweden and abroad, such as molecular data (e.g. omics), imaging techniques
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neuronal connectivity in the brain. With live cell imaging, genetic perturbation, and transcriptomics (in collaboration: proteomics) we derive mechanistic insight into how ciliary signaling translates