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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
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Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden. The program is funded with
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at SciLifeLab An opportunity to develop an internationally competitive research profile and networks as well as guidance for future career development Guidance on relocating and settling in at KTH and in Sweden
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-of-chemistry/ Umeå University strives to offer an equal environment where open dialogue between people with different backgrounds and perspectives lay the foundation for learning, creativity and development. We
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strives to offer an equal environment where open dialogue between people with different backgrounds and perspectives lay the foundation for learning, creativity and development. We welcome people with
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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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understanding their pharmacokinetic properties including how the compounds are metabolized to ensure safe and effective drug development. Data-driven life science (DDLS) uses data, computational methods and
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development of phylogenetic methods. The EvonetsLab is supported by a Starting Grant from the European Research Council and a DDLS Fellowship from the SciLifeLab and Wallenberg Swedish program for data-driven
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dynamic and international research environment, where interdisciplinary researchers work together closely to push the boundaries of RNA biology. Our team has expertise in wet-lab methods development – as
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. The student will work in a group addressing all these challenges, developing new AI-based methods to improve biological realism in simulations which will lead to more accurately inferred GRNs from real data