462 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" uni jobs at The University of Arizona in United States
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this description as organizational priorities and institutional needs evolve. Minimum Qualifications Bachelor's degree or equivalent advanced learning attained through professional level experience required. Minimum
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, and Abilities: Proficient with using a computer and Microsoft Office programs. Excellent customer service skills, including professional phone etiquette. Ability to type on a computer 40 words per
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visitors. Basic computer skills required to check emails, take required University trainings, etc. Performs additional duties as assigned or required to meet Housing & Residential Life and University goals
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excellent communication and interpersonal skills, and a high degree of computer proficiency. This position is a member of the Philanthropy Alumni & Engagement Program (PAE) and subject to joint management by
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demonstrations. Knowledge, Skills & Abilities: Strong understanding of agronomic and weed management principles, operations and practices. Basic computer skills. Ability to work well in a team environment. Ability
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the day-to-day maintenance, organization, and operation of the front desk and reception area, including, but not limited to, the ordering of office supplies and postage, copy machine maintenance, and
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associated computer systems, following established safety and performance protocols. Collaborate with engineers and scientists to contribute to the design, testing, and improvement of optical and mechanical
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) to perform systematic sky surveys for asteroid detection and discovery of new objects. Manage the operation of telescopes, cameras, and associated computer systems, following established safety and performance
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freezers and coolers experience. Repair and maintenance of chillers and boilers experience. Repair of ice machines experience. Commercial, industrial and residential HVAC systems experience. FLSA Non-Exempt
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to Computer Vision models. Specifically, the research aims to study weaknesses of the models when deployed in the real world. Such weaknesses include false prediction and biased inference on complex user data