321 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" Fellowship positions in Norway
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- University of Oslo
- UiT The Arctic University of Norway
- University of Stavanger
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- NTNU Norwegian University of Science and Technology
- University of Agder
- OsloMet – Oslo Metropolitan University
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- CICERO Center for International Climate Research
- CMI - Chr. Michelsen Institute
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
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- Nord University
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hypotheses related to metabolic rate increases, energy allocation shifts, temperature-dependent bioaccumulation, and varying toxicity across biological levels. For more information and how to apply: https
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recently funded centre of excellence (Integreat). Integreat collects scientists from statistics and computer science and offers a flourishing machine learning community, including many PhDs and PostDocs
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educational system. Strong background in molecular modeling, molecular dynamics simulations, or computer-aided drug design. Proven record of programming language through publicly available Github/Gitlab
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dynamics simulations, or computer-aided drug design. Proven record of programming language through publicly available Github/Gitlab or similar repositories. Experience in the cell biology lab Fluent oral and
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of the post. Candidates without a master’s degree have until 1st of July 2026 to complete the final exam. Strong programming and artificial intelligence/machine learning skills. The candidate’s research
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-supervised by Prof. Marianne Fyhn: https://www.mn.uio.no/ibv/personer/vit/rafalc/ https://www.mn.uio.no/ibv/english/people/aca/mariafy/ Jarli og Jordan/UiO via Unsplash Jarli og Jordan/UiO What skills
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kind of machine learning algorithm, provides more accurate data than traditional data collection methods, e.g. paper-based surveys. This data is valuable to several stakeholders: i) architects and urban
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understanding of how acoustic waves are generated and transmitted in wells. The LeDAS project aims to overcome these challenges by combining physical modelling, advanced signal processing, and machine learning in
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laboratory analytical methods (e.g., chromatography, mass spectrometry). Familiarity with AI or machine learning applications relevant to environmental data analysis. Basic knowledge of GIS/mapping tools
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plays in society and how professional language functions, is learned, and used in different contexts. Participants in work package 2 will study how professional language is used in education, research