391 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" research jobs in Norway
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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
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(NICE) Norwegian Centre for Intelligent Computers and Electronics - NTNU. https://www.ntnu.edu/nice For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/294530/phd
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95 94 31 Deadline for applications is 5 May 2026. This date also applies to the submission of publications/written work. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/298248
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of Oslo that spans molecular, cellular, and systems-level approaches. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/295465/postdoctoral-research-fellowship-in
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outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Desirable experience (in ranked order): • Research in
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positions are closely linked, and candidates will have the opportunity to collaborate together and learn methods from the parallel project, building a broad skillset. Both positions are part of the larger
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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