255 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" research jobs at University of Oslo
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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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collaboration with world-leading international collaborators at institutions such as Netherlands Cancer Institute and Karolinska Institutet. For more information and how to apply: https://www.jobbnorge.no/en
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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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with the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The research group on statistical models
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Sciences, Department of Teacher Eduaction and School Research PhD Research Fellow in Education: Learning, Reading, and Assessment in the Context of Generative AI Apply for this job See advertisement Job
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the application. Details about the groups may be found using the following links: https://www.mn.uio.no/math/english/research/groups/several-complex-variables/index.html https://www.mn.uio.no/math/english/research
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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