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and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data
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spectrum, in topics in virology and immunology, and currently specializes in computational biology focusing on developing methods and applications of deep learning for protein sequence and structure, as
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hampers our ability to establish causal relations between molecular alterations and disease phenotypes. In this PhD you will address this by developing a deep learning model of cancer. The PhD position
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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 Microorganisms drive biogeochemical cycles and underpin food webs making them essential for the health and functioning of marine ecosystems. At the Baltic Sea Centre at Stockholm University research
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validation of assays, development, and reporting of automated assays with appropriate throughput for the projects are important. Raw data is reported to web-based databases. You will actively participate in
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developing new methods that can be applied to improve personalized cancer treatment. We are looking for a passionate System developer ( full-stack developer) to participate in our precision cancer medicine
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phylogenetic analysis over traditional Bayesian methods, and this capacity for improvements will have substantially more impact on the more complex MSC model. The project will develop an efficient framework
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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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the development and use of complex model systems that enable the study of biological functions at the multicellular, cellular, and molecular levels under conditions resembling the body’s tissues or organs. This 6