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at least 1 million DNA barcodes. The project involves collaboration with a computer vision lab at Linköping University, focused on developing AI-assisted techniques for picking out specimens for genome
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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include Drive research projects which include analysis of tissue images from multiplex immunofluorescence, spatial proteomics and transcriptomics Drive development of deep learning and computer vision tools
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collaborations. The research group The position is in Ben Murrell’s group in MTC, based in the Biomedicum, in Karolinska’s Solna campus. The lab has worked across the experimental/computational interdisciplinary
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of image analysis and machine learning with a minimum of 90 higher education credits. Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural
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. The research group is part of the National Program for Data-driven Life Science (DDLS), generously funded by the Knut and Alice Wallenberg Foundation: www.scilifelab.se/data-driven/ Our group focuses on studying
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the Royal Institute of Technology, Stockholm. Dahlin’s team works at the intersection between experimental and computational medicine to map blood cell development at the single-cell level. This is performed
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identify systems-level mechanisms in cancer that can be used to uncover new biomarkers, drug targets, and paths to drug resistance. The long-term goal of our lab is to enable computer-aided design of
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will combine state-of-the-art computer vision, modeling and archived specimens to determine biotic and abiotic factors driving spatial variation in molt phenology. It will use museum genomics to recover
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. Our mission is national, and the vision is to enable SciLifeLab to offer high-quality and openly accessible learning resources, such as courses, materials, and other forms of lifelong learning