179 machine-learning "https:" "https:" "https:" "https:" "https:" positions at University of Texas Rio Grande Valley
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experiences. Teach and serve in the Engineering Technology program and support other college programs (Mechanical, Electrical, or Computer Engineering) by offering select courses on the Brownsville campus
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Posting Details Position Information Posting Number SRGV8800 Working Title ADMINISTRATIVE ASSISTANT II Number of Vacancies 1 Location Edinburg, Texas Department The Learning Center FTE 1.0 FLSA Non
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required. A Ph.D. in Computer Science or a related field is required to teach graduate-level courses. Discipline Specific Required Qualifications Preferred Qualification Teaching experience is desirable, as
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. Provides operational and documentation support for assurance-of-learning and accreditation activities, including AACSB reporting and maintenance of required records. Maintains frequent interaction with
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of Job To support the operation, coordination, and execution of Career Center programs and initiatives, including career readiness, employer engagement, and experiential learning efforts. Responsible
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medical school is preferred. The chosen individual will assist in implementing a vertically and horizontally integrated curriculum utilizing active, team based, and problem based learning, flipped classroom
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Tenure Status Non Tenure Track FTE 1.0 Scope of Job The School of Art and Design at The University of Texas Rio Grande Valley (UTRGV) is hiring One (1) one-year lecturer to start Fall 2025 to teach in its
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of Job The Department of Electrical and Computer Engineering at The University of Texas Rio Grande Valley (UTRGV) occasionally has the need to hire non-tenure track, One-Year Lecturers to teach in its
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or a strong aptitude for using advanced computational tools, AI, or machine learning techniques to address engineering challenges; and interest or initial experience in interdisciplinary collaboration
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journals or conferences; experience with or a strong aptitude for using advanced computational tools, AI, or machine learning techniques to address engineering challenges; and interest or initial experience