46 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" PhD positions in Norway
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that the PhD candidates complete their degrees within the nominal length of study an attractive and good learning environment for PhD candidates The programme offers several courses and candidates from other
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introduce fundamental challenges related to guidance, control, coordination, and human–machine interaction. This PhD position addresses these challenges through the development of high- Technology Readiness
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with algorithms for wearable data University of Manchester (UK): To learn mathematical modelling of hormone rhythms. University of Bristol (UK): To learn mathematical modelling of hormone rhythm
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-year master’s degree or a cand.med.vet. degree, with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second cycle. The applicant must have a documented
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the nominal length of study an attractive and good learning environment for PhD candidates The programme offers several courses and candidates from other programmes may apply for admission to the individual
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must have an academically relevant education corresponding to a five-year master’s degree or a cand.med.vet. (veterinary) degree, with a learning outcome corresponding to the descriptions in
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characteristics To complete a doctoral degree (PhD), the candidate is expected to: demonstrate strong motivation, curiosity, and a learning-oriented mindset work independently, take initiative, and maintain good
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critically and will work both independently and collaboratively within the project consortium. You should enjoy interdisciplinary research and take keen interest in learning and working as part of
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both NMBU and NTNU. The applicant must have an academically relevant education corresponding to a five-year master’s degree, with a learning outcome corresponding to the descriptions in the Norwegian
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in English Solid knowledge in finite element analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks