138 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" PhD positions in Norway
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information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/292175/phd-position-on-the-history-of-medical-plastics Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/292175
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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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maintaining creative autonomy and avoiding the risk of machine-driven homogenisation? What are the cultural, ethical, and market implications for artists and audiences when AI systems integrate into creative
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Norwegian master’s degree. Please see https://hkdir.no/en/ for more information about HK-dir’s general recognition. The review process by HK-dir may take some time. Therefore, please submit your application
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
(as machine learning techniques, etc.). Personal characteristics In the evaluation of which candidate is best qualified for the PhD position, emphasis will be placed on education, experience and
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with disabilities. More information about NMBU is available at www.nmbu.no/en Questions about the position Volha Shapaval Professor +47 98418248 volha.shapaval@nmbu.no Where to apply Website 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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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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Master’s degree. Please see https://hkdir.no/en for more information. The approval process will take some time and we recommend you to contact HK - dir as soon as you decide to apply for the position
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systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI outputs in a known structure. This