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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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the central node. The work includes developing scalable methods for information compression, robust detection of faulty or malicious nodes, and principles for handling uncertain or varying sensor quality
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resonance imaging, and behavioral testing in a large sample of individuals experiencing intense grief at least twelve months after bereavement. In this way, the thesis will create a multimodal and multilevel
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imaging, mathematical modelling, and functional genomics, receiving experimentally testable predictions generated by state-of-the-art predictive models. These predictions will be rigorously validated using
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student will use various experimental platforms within LUMIA (LUMIA - Luleå Material Imaging and Analysis ) as well as national research infrastructures through MAX IV Laboratory, SciLifeLab, ARTEMI, and
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. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential
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. You will also characterize the eye using in vivo imaging, histology, immunohistochemistry, protein analysis, and advanced sequencing and transcriptomic approaches. Experimental therapies administered
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algorithms for Bayesian machine learning with applications in e.g., medical image analysis. The doctoral student position is offered within the machine learning project “The Challenges for Machine Learning in
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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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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