55 computer-programmer-"Multiple"-"U"-"Humboldt-Stiftung-Foundation"-"U.S"-"Prof" positions at SciLifeLab
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& Wallenberg National Program for Data-Driven Life Science (DDLS), a 12-year initiative aimed at recruiting and training the next generation of data-driven life scientists and to create globally leading
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) programme and research school Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures
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other experts within the Support for Computational Resources unit at NBIS and related organisations, notably SciLifeLab, National Academic Infrastructure for Supercomputing in Sweden (NAISS), and EuroHPC
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and Wallenberg National Program for Data-Driven Life Science (DDLS) (https://www.scilifelab.se/data-driven/ ) aims to recruit and train the next generation of data-driven life scientists and to create
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health and global ecosystems. The SciLifeLab 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
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National Program for Data Driven Life Science (DDLS). About the DDLS Fellows program Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems
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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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stakeholders at all SciLifeLab sites in Sweden, e.g. representing the technology platforms, the national data centre, the operations office, the training hub, the data-driven life science (DDLS) research program
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Uppsala and in Sweden at large. For information about the SciLifeLab fellow program, see https://www.scilifelab.se/research/#fellows. SciLifeLab Fellows are also part of a broad national network of future
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factor – DNA binding, detecting protein – protein interactions or enzyme optimization. Main responsibilities The candidate will use and develop methods within one, or preferably multiple, of the following