598 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" positions in Norway
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quantitative research methods and the ability to teach and supervise at PhD level. The successful candidate is expected to take an active role in developing high-quality national and international research
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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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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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(NELO), revealing the branchial cavity as an important region of the fish immune system. (https://www.science.org/doi/10.1126/sciadv.adj0101 ) (https://www.frontiersin.org/journals/immunology/articles
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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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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
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Computers and Electronics - NTNU. https://www.ntnu.edu/nice Jarli & Jordan/ UiO via Unsplash Jarli & Jordan/ UiO What skills are important in this role? The Faculty of Mathematics and Natural Sciences has a
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protection. For more information about the Digital Security Research Group, see https://www.mn.uio.no/ifi/english/research/groups/sec/ Jarli & Jordan/ UiO via Unsplash Jarli & Jordan/ UiO What skills
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stimulating research environment. The project will be supervised by Prof. Rafal Ciosk and co-supervised by Prof. Marianne Fyhn: https://www.mn.uio.no/ibv/personer/vit/rafalc/ https://www.mn.uio.no/ibv/english
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. Demonstrated experience applying machine learning and AI-based approaches to empirical disease, ecological, or biological datasets, with an emphasis on pattern identification, prediction, or spatial risk mapping