114 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" research jobs at Cornell University
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veterinary technician simulation training as well as internal and external continuing education. This experience will provide the foundation necessary to: identify the learning needs of diverse audiences
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Postdoctoral Associate to begin July 1, 2026. The one-year term position is renewable for an additional year based on performance and is part of Cornell’s Active Learning Initiative . This initiative supports
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Associate as part of Cornell’s Active Learning Initiative (https://teaching.cornell.edu/active-learning-initiative-0) for the AYs 2026 – 2028. We invite applications from candidates with a specialization in
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about the ILR School can be obtained at our web site, http://www.ilr.cornell.edu and CAROW at https://www.ilr.cornell.edu/carow . Requirements: Applicant must have a PhD in labor relations, sociology
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, nutrition lesson) and bi-weekly they will engage in a self-guided culinary session at home (prepare an ethnic, plant-based meal). To learn more visit https://www.aceprogramnyc.com/ . 2) The Double Up Foods
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, United States of America [map ] Subject Area: Computer Science / Artificial intelligence and machine learning Appl Deadline: 2025/11/21 04:59 AM UnitedKingdomTime (posted 2025/10/23 05:00 AM UnitedKingdomTime) Position
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comparative genomics, chromatin architecture, gene expression, protein abundance, and metabolite profiling—combined with computational biology, machine learning, and advanced statistical methods. Supported by
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at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and researchers across learning sciences, computer science, machine learning
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sciences, computer science, machine learning, and education research. Research Role Research themes for the NTO Postdoctoral Associate include, but are not limited to: Developing or evaluating methodologies
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crane. The successful candidate will build reproducible machine learning pipelines, integrate detections into spatial ecological models, and generate conservation-relevant outputs for regional partners