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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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the peculiar optical properties of nanostructures and state of the art instrumentation for optical imaging. The methodology will be explored within the context of drug-target interactions of importance to early
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project. The project will also employ a PhD student at Lund University, focusing on developing hybrid architectures for deep learning-based image processing and methods for multimodal medical data. We will
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, spectroscopic signatures, microstructural images, processing conditions, and macroscale performance will be used for the optimization of materials. The candidate will collaborate extensively with in
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multidisciplinary environment at the interface between academia, clinic, pharma industry, where resources and expertise in human biobanking, omics data, imaging, animal surgery and molecular biology methods will be
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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 also have substantial experience in a laboratory research environment. You should have prior experience with experimental molecular biology techniques, tissue processing and imaging. You also have
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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