176 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" uni jobs at University of Utah
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responsible for many of the U’s shared IT services including the wired and wireless network; Campus Information Services (CIS) portal; UMail, telephone, and online collaboration; digital learning technologies
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Skip to Main Content Toggle navigation Home Search Jobs Job Alerts Log In /Create Account Help UU Student - Computer Bookmark this Posting Print Preview | Apply for this Job Please see Special
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that supports one scheduling team and/or one referral rotation. The Customer Advocate Specialist I (CAS I) will learn the essential Electronic Medical Record (EMR) programs and processes required for both patient
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that supports one scheduling team and/or one referral rotation. The Customer Advocate Specialist I (CAS I) will learn the essential Electronic Medical Record (EMR) programs and processes required for both patient
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Engineering City Salt Lake City, UT Track Tenure Track New Position to Begin 1 Jul 2026 Details The Department of Electrical and Computer Engineering at the University of Utah seeks faculty candidates who can
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motivated and pay attention to detail with a passion to provide excellent patient care in a fast paced and evolving environment. Ability to work efficiently and independently. Demonstrated computer skills and
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wireless network; Campus Information Services (CIS) portal; UMail, telephone, and online collaboration; digital learning technologies; information security; software licensing; and a host of other IT systems
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wireless network; Campus Information Services (CIS) portal; UMail, telephone, and online collaboration; digital learning technologies; information security; software licensing; and a host of other IT systems
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Date 12/22/2025 Requisition Number PRN43922B Job Title Web Administrators Working Title Web Content Specialist Career Progression Track P00 Track Level P2 - Developing FLSA Code Computer Employee Patient
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and non-technical audiences. Machine Learning (Plus): Utilize machine learning techniques to analyze health outcome data, develop predictive models, and identify patterns and trends in large datasets