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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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description The project focuses on studying the evolution of evolvability using computational simulations. Evidence from evolutionary developmental biology suggests that evolvability can change rapidly in
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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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research and methodological development to design and implement novel computational models and solutions. A solid theoretical background and hands-on experience in digital image processing and deep learning
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supported by the SciLifeLab and Wallenberg National Program. The research group at the Department of Medical Biochemistry and Microbiology where the candidate will be joining is focusing on mechanisms and
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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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. The project will develop fundamental theory and tools that will be key for understanding biological mechanisms causing diseases that are due to gene dysregulation, such as cancer. The core of the project is a
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your application! We are looking for a PhD student in evolutionary genetics interested in contributing to a better understanding of the mechanisms that shape mutation rates. Your work assignments
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networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
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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