176 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" scholarships in Norway
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- NTNU - Norwegian University of Science and Technology
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demonstration of a methodology for building and integrating machine learning solutions for past technical artefacts. Contributing to the development of holistic view of product lifecycle, its digital artefacts
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/ ). By combining advanced machine learning techniques with qualitative methods, the project will investigate usage patterns and engagement levels with a health app across multiple European countries
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/ ). By combining advanced machine learning techniques with qualitative methods, the project will investigate usage patterns and engagement levels with a health app across multiple European countries
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(viability, proliferation, outgrowth, and invasion assays) is desirable. Experience with, or interest in, machine learning for the analysis of microscopy data and a strong ability to collaborate with
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
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education. You must meet the requirements for admission to the doctoral program from the Faculty of Engineering (https://www.ntnu.edu/studies/phiv ) You must be fluent in spoken and written English. PLEASE
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area on civil security by focusing on the vulnerability assessment of urban environments, sustainability, resilience, and knowledge for a better world (https://www.ntnu.edu/civil-security ). Duties
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modelling, and high-performance computing. The position offers close supervision, access to modern computational infrastructure, and collaboration opportunities across disciplines — from mechanics to machine