177 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" scholarships in Norway
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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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Interaction (HCI). The position will collaborate closely with (1) the NTNU Shore Control Lab (SCL), a design and human factors research environment focusing on human-centered design of technology for safety
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the Research Council of Norway and the Department of Psychology at the University of Oslo (UiO). Learn more about working at PROMENTA here . About the NeuroPathways Convergence Environment and the PhD project
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information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/292175/phd-position-on-the-history-of-medical-plastics Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/292175
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and milestones Contribute to a positive and collaborative group atmosphere, be open to learn and teach Strong communication and presentation skills Be flexible and open to adjusting the plan
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research disciplines. For further information about the different research disciplines see https://www.ntnu.edu/imf/research . Are you motivated to take a step towards a doctorate and open exciting career
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health, epidemiology, statistics, biostatistics, or machine learning/artificial intelligence. You must have a strong academic background from your previous studies and have an average grade from your
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-year master’s degree, with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second cycle. The applicant must have a documented strong academic background
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are particularly suitable for a PhD 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
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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