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evaluate large language models (LLMs) assisted methods for extracting, organising and assessing complex scientific information related to materials. The initial application will be phase diagrams and related
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heterogeneous sources of information—including remote sensing, forest inventory data, existing forest maps, environmental and climatic data, and emerging large-scale AI representations—can be jointly exploited
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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data. Experience with FITACF, PyDARN, GUISDAP and RST data analysis tools. Demonstrated experience of working with or developing AI tools for analysis of large datasets. Authorship or co-authorship in
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join the European FALCO project (Fighting Addictions, Improving Lives: Comprehensive Drug Rehabilitation with Music), a large international randomized controlled trial investigating the effects of music
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logistics Analyse and interpret large-scale molecular data (approximately 11,000 proteins per project) in collaboration with our bioinformatics and statistics collaborators Publish results in international
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Annie Spratt 18th October 2026 Languages English Norsk Bokmål English English PhD Scholarship - AI for Forest Robotics Apply for this job See advertisement Key Information The PhD fellowship aims
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18 Sep 2026 Job Information Organisation/Company OsloMet – Oslo Metropolitan University Research Field Geography » Human geography Political sciences » Public policy Political sciences » Other
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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Sciences, specialization Business Administration, Innovation and Governance (BIG). The successful candidate is expected to complete the doctoral degree during the period of employment. Skjalg Bøhmer Vold