397 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" uni jobs at University of New Mexico
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Families Department fingerprint screening, verification of education credentials, and/or verification of prior employment. For more information about background checks, visit https://policy.unm.edu
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information about background checks, visit https://policy.unm.edu/university-policies/3000/3280.html . Refer to https://policy.unm.edu/university-policies/3000/3200.html for a definition of Regular Staff
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to apply computational approaches to research questions. Experience or familiarity with artificial intelligence and machine learning methods is highly desirable. Prior research experience in a laboratory
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fingerprint screening, verification of education credentials, and/or verification of prior employment. For more information about background checks, visit https://policy.unm.edu/university-policies/3000/3280
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information about background checks, visit https://policy.unm.edu/university-policies/3000/3280.html . Refer to https://policy.unm.edu/university-policies/3000/3200.html for a definition of Regular Staff
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Career Opportunity site https://cdd.health.unm.edu/jobs/ See the Position Description for additional information. Conditions of Employment If this position is assigned to the Athletics Department, either
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check out our Career Opportunity site https://cdd.health.unm.edu/jobs/ See the Position Description for additional information. Conditions of Employment Possession of a valid New Mexico driver's license
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the ECHO Institute’s WHO Collaborating Centre for Digital Learning in Health Emergencies. Your responsibilities as the Program Manager will include: Managing the operational planning, establishment
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communities with multidisciplinary teams of experts, enabling learning, mentorship, and systems-level transformation. Proven to be scalable and impactful across health, education, and social systems, the ECHO
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growth and accreditation requirements, ensuring compliance and operational efficiency Model utilization (student counts, cohort mix, lab hours, scenario throughput) Tie spending to learning outcomes and