609 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" positions in Norway
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period of three years. The position is located at the University of Agder’s Kristiansand campus. The proposed starting date is the 1st of September 2026. About the Faculty: https://www.uia.no/en/about-uia
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the Civil Servant Act, the Security Act, and the Export Control Act. Interested in learning more about the position? We are happy to tell you more about life on campus. Contact associated professor Davide
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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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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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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
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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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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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of AI and in particular machine learning (ML). As today’s mainstream AI/ML workloads often resort to large-scale and energy-hungry supercomputers, it is necessary have a more critical look at how HPC