583 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" positions in Norway
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: https://www.mn.uio.no/math/english/research/groups/several-complex-variables/index.html https://www.mn.uio.no/math/english/research/groups/operator-algebras/index.html The Faculty of Mathematics and
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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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dissertated before the start-up date of the position. A research profile with relevant experience in biological sequence analysis, with complementary skills in machine learning or other relevant algorithms. A
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material waste and environmental impact, enabling sustainable processing of healthy and safe foods. Write scientific publications. Teach on Food Processing courses:Food Processing II (MVI261) and Food
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English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements
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. Further information about the research groups can be found at: https://www.mn.uio.no/math/english/research/groups/algebra/index.html https://www.mn.uio.no/math/english/research/groups/geometry-topology
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https://www.ntnu.edu/imf/research/ for an overview. Your immediate leader will be the Head of Department. Duties of the position Carry out research of good quality. Academic publications. Participate in
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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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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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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