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connection to the research environment, and a comprehensive national and international research network and good industrial and professional collaboration. Please refer to Department of Animal and Veterinary
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Do you have a computational background and interest in developing novel tools for integrating structural biology data to understand regulation of disordered regions of proteins? Then the Viennet lab
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project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer engineering
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Do you have a computational background and interest in developing novel tools for integrating structural biology data to understand regulation of disordered regions of proteins? Then the Viennet lab
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to protein engineering and molecular cloning, Experienced in bioinformatic genome analysis and computational tools related to protein structure analysis and prediction, Sufficient expertise in standard
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recognised track records. CNAP participates in numerous international initiatives and maintains an extensive global network, making it an ideal environment to build your own collaborative connections. CNAP is
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education. We offer modern laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international research network
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Postdoc in assessing carbon sequestration potential of different wetlands as nature-based solutio...
-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international researcher network. The department consists of nine research sections with
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-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international researcher network. The department consists of nine research sections with
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with