241 computer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" "https:" "https:" "https:" positions at University of New Mexico
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with students, faculty, and staff. Additional Requirements Campus Main - Albuquerque, NM Department RLSH Res Ed Program (215F0) Employment Type Staff Staff Type Term - Full-Time Term End Date 06/30/2028
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are eligible for the Veteran Preference Program. See the Veteran Preference Program webpage for additional details. The University of New Mexico is committed to hiring and retaining a diverse workforce. We are
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experience matches the position as described above; including any Preferred Qualifications. Positions posted with a Staff Type of Regular or Term are eligible for the Veteran Preference Program. See the
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that addresses our preferred qualifications. 4. Incomplete applications will not be considered. Positions posted with a Staff Type of Regular or Term are eligible for the Veteran Preference Program. See
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from supervisors Positions posted with a Staff Type of Regular or Term are eligible for the Veteran Preference Program. See the Veteran Preference Program webpage for additional details. The University
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on a year for year basis. Preferred Qualifications Strong computer and data entry skills Experience with calendaring, meeting coordination, and note-taking Proficiency in Smartsheets, Microsoft Office
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Type of Regular or Term are eligible for the Veteran Preference Program. See the Veteran Preference Program webpage for additional details. The University of New Mexico is committed to hiring and
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Regular or Term are eligible for the Veteran Preference Program. See the Veteran Preference Program webpage for additional details. The University of New Mexico is committed to hiring and retaining a
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an application. Resume and UNM jobs application. Positions posted with a Staff Type of Regular or Term are eligible for the Veteran Preference Program. See the Veteran Preference Program webpage for additional
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) and research teams to help answer biological questions by applying computing and data science tools to large data sets, and iterate efficiently to deliver reproducible, publication-quality results