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Duties: The Teaching Assistant Professor of Educational Statistics and Research Methods is a non-tenure track position hired to teach in the newly developed graduate programs focused on Healthcare Business
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Resources at 870.575.8400 . Department: Math & Computer Science Department's Website: Summary of Job Duties: Teach undergraduate courses in Mathematics, Computer Science, Statistics, or Data Science according
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Resources at 870.575.8400 . Department: Math & Computer Science Department's Website: Summary of Job Duties: Teach undergraduate courses in Mathematics, Computer Science, Statistics, or Data Science according
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the quality of research conducted at UAMS by providing expert statistical guidance to UAMS investigators. With minimal guidance of Biostatistics Faculty or advanced research staff, the Biostatistician II
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of research conducted at UAMS by providing expert statistical guidance to UAMS investigators. With minimal guidance of Biostatistics Faculty or advanced research staff, the Biostatistician II commonly assists
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at the instructor or professorate level to be determined by qualifications. Teach a wide range of undergraduate mathematics and statistics courses including general education, service, and mathematics/statistics
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maintain collegial, harmonious working relationships with others. Qualifications: Minimum Qualifications: Formal education equivalent of a master’s degree in Statistics, Analytics, Computer Science, or a
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the food system -Review scientific and applied literature to identify appropriate materials, statistical techniques, or methods applicable to the area of research. -Train undergraduate and graduate students
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of administrative support or program coordination experience required. Experience with data analytics Preferred Qualifications: Master's degree in Business Administration or related field Experience with statistical
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proficiency in both field and lab equipment operation, safe driving, safe use of ATVs, computer skills including GIS, GPS, and statistical packages, knowledge of soil sampling methods, sample processing, and