883 computional-geometry-algorithm uni jobs at University of Texas at Austin in United States
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Education, Attending to Place and Context, and Thriving through Transitions. Qualifications Required Qualifications Ph.D. in Computer Science, Data Science, Quantitative Psychology, Biostatistics, or a
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of all grant activities, correspondence, reports, procedures, and presentations related to the GEAR UP program; Develop outreach materials, including newsletters and social media sites. Function 5
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teams. Shares knowledge and resources. Supports team members Required Qualifications Bachelor's degree in Computer Science, Information Technology, or a related field. 4 years of experience developing
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Compensation program 457(b). • Flexible spending account options for medical and childcare expenses. • Robust free training access through LinkedIn Learning plus professional conference opportunities
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. Accuracy and timeliness of dailies, mileage reports, and computer-oriented weeklies filled out correctly and submitted on time each week. Work orders filled out correctly. Notes on work orders indicate what
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) and a Deferred Compensation program 457(b) Flexible spending account options for medical and childcare expenses Robust free training access through LinkedIn Learning plus professional conference
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. Perform cost analysis and estimates. Other related functions as assigned. Required Qualifications A bachelor’s degree in Computer Science, Software Engineering, or equivalent or 4 years of relevant
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advising and help for Spring/Summer/Fall Orientation sessions throughout the year. CNS Advising Program Oversight and Implementation. Each Senior Academic Advisor leads specific programs or processes for CNS
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Qualifications Experience working with bacteria through genetic and biochemical means. Previous small animal experience. Computational skills in R or python. Salary $45,000 Working Conditions Uniforms and/or
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across the college (e.g., Program Offices) to share data and insights and foster a culture of data-informed decision-making. Identify opportunities for data-driven innovation in Career Management, such as