600 machine-learning "https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" positions in Norway
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consist of 15 PhDs, that together will work and learn as an international cohort and collaborate across projects to bring together insights anchored in different sectors and countries. The candidates will
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, Marineholmen, Møllendalsveien and Årstad. There are seven departments and several centres at Faculty of Science and Technology. Read more about the faculty and departments. Where to apply Website https
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to the international initiative “Antarctic InSync.” For more information, see: https://ic3.uit.no/ . The position is based in the Marine Ecology section of the Research Department and will involve collaboration within a
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affiliated with the Centre, and opportunities for research stays with partners, both nationally and abroad, through its international network. Read more about the Centre and its research at the website: https
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techniques to identify the enzymes, as well as protein purification and recombinant protein expression to get hold of the enzymes. The PhD student will be part of the large and international PEP group (https
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grade background, you may be considered if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme: https
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if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme (https://www.ntnu.no/studier/phet) You must
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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other
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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned
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analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks on material design by using PyTorch or Matlab PLEASE