332 machine-learning "https:" "https:" "https:" "https:" "U.S" "U.S" Fellowship positions in Norway
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- University of Oslo
- UiT The Arctic University of Norway
- University of Bergen
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- NTNU - Norwegian University of Science and Technology
- University of Agder
- NTNU Norwegian University of Science and Technology
- University of Inland Norway
- CICERO Center for International Climate Research
- CMI - Chr. Michelsen Institute
- Høgskulen i Volda
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- NORCE Norwegian Research Centre
- Nord University
- Norwegian Institute of Bioeconomy Research
- Simula Research Laboratory
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- Østfold University College
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developed countries, smartphone penetration exceeds 80%. The automatic transport mode detection (TMD), when effectively exploited, possibly using some kind of machine learning algorithm, provides more
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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questions and data of CREATE. The successful candidate will conduct advanced methodological and psychometric research. Potential topics include (a) AI, machine learning, and large language models
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processes based on mobile digital technologies increase, so do the amount and severity of cyber threats. Both defenders and attackers are now using Machine Learning (ML) and Artificial Intelligence (AI
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, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish an internationally
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depend on the candidate’s qualifications and CREATE's needs. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/295895/phd-research-fellow-in-social-sciences Where
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, you will conduct cutting-edge research in these areas. You will learn state-of-the-art techniques in formal methods and knowledge representation and apply them to high-impact use cases related
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related to models and multiple sources of data describing ecological dynamics. The PhD project will address the following aims: 1) Develop efficient tools for learning about models from data, 2
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Environment Convergence Environment. Clim-SHOCK investigates volcanic climate shocks from the past and places them into a future scenario. What can we learn from the past to improve future climate projections
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) to enhance AML capabilities. AI-driven solutions can learn from vast datasets to spot hidden patterns and anomalies beyond human or rule-based detection. For more information and how to apply: https