631 machine-learning "https:" "https:" "https:" "https:" "U.S" "U.S" positions in Norway
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of these groups is invited for an interview as part of our commitment to inclusivity and equal opportunity. For further information about the recruitment process, click here . Where to apply Website https
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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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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
Language requirement: Good oral and written communication skills in English English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no
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may be considered if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme (https://www.ntnu.no
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an international and interactive environment. Candidates must have extensive experience with: Programming (including MATLAB) and computational modelling Machine Learning (ML) methodology applied for complex data
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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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programmes is a primary focus for the faculty's doctoral programme, PhD in educational sciences (UTVIT) (https://www.inn.no/english/research/doctoral-degree/educational-sciences/). It is a prerequisite that
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the researchers from Department of Automation and Process Engineering will play a key role. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early
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Science About the project This PhD project integrates pharmacoepidemiology, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies
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the areas of stochastic analysis and computational methods towards machine learning with focus on risk-sensitive decision making and control. Techniques may include forward, backward stochastic differential