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applications for a PhD position focused on developing a theoretical framework for monitoring and updating adaptive learning systems (including machine learning/artificial intelligence systems) under formal
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computational physics and/or geosciences and a strong interest in machine learning, Earth observation, and numerical modelling for Arctic prediction. The PhD project will develop methods to improve monitoring and
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complementary and synergic methods at the intersection of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation
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-principles electronic structure calculations Perform materials screening including machine learning to identify promising thermoelectric materials for cooling technology The successful candidate is expected
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Learning, Natural Language Processing, Human-Computer Interaction, Digital Health, Endocrinology Secondments (Preliminary Plan): UiB (Norway): 1–2 months — Patient and caregiver interviews, exploration
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, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling. The centre has numerous collaborations with leading biomedical research groups internationally and in
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work that covers all the major fields in data science and operations research and core topics in machine learning and computer science relevant for a PhD in Data and Decision Sciences. These courses
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Technology (NTNU) for general criteria for the position. Desired qualifications Applicants should possess a basic understanding of key AI concepts (machine learning, neural networks, prompt engineering, human
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Norwegian University of Science and Technology (NTNU) for general criteria for the position. Desired qualifications Applicants should possess a basic understanding of key AI concepts (machine learning
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machine-learning methods to enhance predictive capability and enable adaptive process control. Experimental work will include laboratory- and industrial-scale forming trials, supported by comprehensive