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Description The Subhani Department of Computer Information Systems at Texas A&M University–Central Texas is seeking applicants for Fall 2025 adjunct instructor positions. Successful candidates will be expected
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, and Abilities: Ability to multi-task and work cooperatively with others. Basic computer skills. Ability to deliver or pick up supplies. The well qualified candidate will also possess an associate
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communication, and use of platforms such as Howdy and e-Campus. Supervises student workers, including hiring, training, scheduling, and task management; oversees mail distribution, equipment upkeep, and special
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(with EMS or other software) d. Operate standard office equipment. e. Receive, sort, route and distribute mail; make copies of handouts; assemble materials for use by the department head and faculty
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, responsible for the planning and implementation of a program to advance cultivation, solicitation, stewardship, retention, and recognition of the university’s generous benefactors. Additionally, the position
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: Degree Plans and Advising – Accesses student records and prepares and distributes official degree plans for all Social Sciences majors. Maintains confidential records for all students, documenting progress
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Position in the Department of Electrical & Computer Engineering. The SMART Center was established by the Chancellor of the Texas A&M University, with multi-million dollar funds to support research work in
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of Aggie Athletics to capture audience attention. Distributes these documents to all news and information media. Responsible for maintaining accurate and up-to-date information on 12thMan.com for assigned
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testing questions in support of a sponsored research program Sorts and distributes incoming mail Assist in keeping gathering areas neat Assists with ordering and storing office supplies Support Service
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., multivariate regression, time series) Experience working with large population dataset Experience in working with high-performance computing (HPC) clusters and distributed systems to process large-scale data