317 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at University of Oslo
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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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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of experts? Then this job may be for you. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/296128/phd-fellowship-in-systems-neuroscience Where to apply Website https
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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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interests through elective courses and secondments. • Blended Learning Approach: Our training combines intensive in-person workshops at partner institutions with regular interactive online seminars, journal
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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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Research PhD Research Fellow in Education: Learning, Reading, and Assessment in the Context of Generative AI Apply for this job See advertisement Job description Applicants are invited to apply for a 3-year
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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