705 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" uni jobs at Cornell University
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course syllabus, content, and assessments Instruct veterinary students throughout the curriculum and extra-curricularly on management of hoof disorders of the horse •Didactic lectures and interactive
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home will be the Nolan School, but it is expected that faculty teach across the three schools within the College of Business. The Cornell Peter and Stephanie Nolan School of Hotel Administration is the
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should submit materials to CornellVPandGC@SpencerStuart.com . For additional information about Cornell University, please visit https://www.cornell.edu/ . The Opportunity The Vice President and General
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learning, intellectual engagement, and hands-on experience with a wide array of cutting-edge techniques in genomics, metabolomics, and microbial research – all within a collaborative and inclusive scientific
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responsibility for teaching in the veterinary curriculum, including facilitating problem-based learning groups in the pre-clinical physiology curriculum (approximately 12 weeks of teaching commitment), and
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responsibilities for academic and nonacademic positions within the department. To apply: Please apply via Academic Jobs Online https://academicjobsonline.org/ajo/jobs/31846 Qualified candidates should submit a short
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: http://cce.cornell.edu/sites/default/files/applying_external.pdf. Internal Applicants: Current employees of Cornell Cooperative Extension of Ontario County are considered internal applicants. Please log
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an appointment through December 31, 2026. More information about the ILR School can be obtained at our web site, http://www.ilr.cornell.edu and the Yang-Tan Institute at http://www.yti.cornell.edu/ . We require
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diverse team and perform with minimum supervision. Have basic computer and technical skills including familiarity with Microsoft Office and the ability to learn electronic medical record system (ezyVet)and
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. The focus of this position is developing methods to disentangle dynamic, multiscale ecological signals from large, noisy observational data. This work lies at the interface of statistics, machine learning/AI