112 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD scholarships in Norway
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mechanistic process models with machine learning for accuracy, generalization, and interpretability. Uncertainty-aware AI: robust inference under noise, drift, and changing conditions; knowing when a model is
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-year master’s degree, with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second cycle. The applicant must have a documented strong academic background
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are particularly suitable for a PhD education. You must meet the requirements for admission to the doctoral program from the Faculty of Engineering (https://www.ntnu.edu/studies/phiv ) You must be
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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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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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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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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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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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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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is available in the group of Dr. Matthijs Dorst (https://dorst-lab.org/ ), with co-supervision by Associate Professor Koen Vervaeke (https://vervaeke-lab.org/ ). The lab is located at the Division