786 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Cornell University
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flexible hours, which may include evenings and/or weekends, as appropriate. Ability to teach informal educational programs. Ability to effectively participate in professional team efforts. Ability to plan
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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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information. Ability to work effectively with college, university, and departmental financial offices. Commitment to continuous learning. Preferred Qualifications Experience in higher education with grant
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about our benefits: https://hr.cornell.edu/benefits-pay . Follow this link to learn more about the Total Rewards of Working at Cornell: https://hr.cornell.edu/jobs/your-total-rewards . Our leave
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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
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applications for a part-time Lecturer position in health administration to teach in our residential and executive masters programs starting in late Spring or early Summer 2026. We are seeking a healthcare
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written communication skills; Computer skills in word processing, spreadsheets and database management; Must be committed to creating and maintaining an environment that fosters positive youth development
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Academic Jobs Online (https://academicjobsonline.org/ajo/jobs/31583 ">https://academicjobsonline.org/ajo/jobs/31583 ). Qualified candidates should submit a short cover letter, curriculum vitae, and contact
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submit a CV, letter of interest, research statement, contact information for 3 references via Academic Jobs Online at the following link: https://academicjobsonline.org/ajo/jobs/31352 ">https
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materials by bringing together computer scientists, materials researchers, and data scientists to tackle knowledge- and data-centric challenges at the intersection of AI and materials science. In this AI-MI