620 machine-learning "https:" "https:" "https:" "https:" "U.S" "U.S" positions in Norway
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research disciplines. For further information about the different research disciplines see https://www.ntnu.edu/imf/research . Are you motivated to take a step towards a doctorate and open exciting career
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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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purpose is teaching and research. All profits are used to strengthen BI's academic- and learning environment. Job description Through its academic work, BI has an ambition to make its mark in
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. The successful applicant must be able to teach at all levels and to supervise Master and PhD students. The successful applicant may furthermore be required to take on other teaching duties and administrative tasks
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criteria see https://www.nmbu.no/en/research/regulations-and-guidelines-doctoral-degrees-nmbu To be employed, you cannot have previously held a PhD position at NIBIO or with funding from The Research Council
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. The project will collaborate closely with, and utilize existing linked data from, Regforsk at NTNU (https://www.ntnu.no/ism/forskning/regforsk#/view/publications). Deakin University in Melbourne, Australia is
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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
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qualifications: A strong background in algebraic topology. Experience in any of the following: Higher category theory, stable homotopy theory, simplicial methods. An interest in computer algebra and programming
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selection criteria Experience with machine learning or other relevant AI technologies Experience with condition monitoring, preferably within maritime domains Knowledge of ship machinery and systems Good oral
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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