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paragraph “About the application”. If you do not already have educational competence that meets the requirements for a position as associate professor in Norway, NTNU will arrange for you to acquire
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at the crossing of statistics and machine learning. Modern vessels produce vast amounts of multivariate data streams. The project addresses the development of trustworthy statistical and machine learning methods
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, machine learning, data-driven dynamics, stability/perturbation theory/rigorous numerics. Possible applications of the new theory include the analysis of spatiotemporal data arising from weather and climate
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program leadership and/or potential mentors, and must contact mentors prior to submitting their application. The ETEP mentor list can be found https://sph.umich.edu/ehs/etep/faculty.html Only full
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Berlin conducts interdisciplinary science to understand, anticipate, and shape major disruptions from Artificial Intelligence (AI) to the way we think, learn, work, play, cooperate, and govern. Our goal is
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encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected
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Fellows. It pays like a strong postdoc. No background in AI is required; we weigh the ability to learn above experience with any method. Top-performing Fellows will be offered permanent research positions
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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 14 hours ago
policy engagement on China through rigorous scholarship and meaningful public outreach. More information about CSKI and the fellowship program is available at https://www.mq.edu.au/faculty-of-arts/schools
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presentations and published works. CRRES fellowships are designed to advance the careers of new scholars by providing opportunities to research, teach, and connect with mentors and with faculty in host
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boosting, and related machine-learning approaches Evaluation of model generalizability using train/test splits, leave-one-cohort-out validation, and leave-one-platform-out validation Assessment of predictive