17 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at University of Texas at Austin
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account options for medical and childcare expenses Robust free training access through LinkedIn Learning plus professional conference opportunities Tuition assistance Expansive employee discount program
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as
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with NCI, CPRIT, and NIH-funded projects. Required Qualifications PhD in computational biology, bioinformatics, computer science, information science, biomedical engineering, or a related field. PhD must
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account options for medical and childcare expenses Robust free training access through LinkedIn Learning plus professional conference opportunities Tuition assistance Expansive employee discount program
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collection, including cognitive assessments such as computer-based testing, questionnaires, and health measurements, as well as brain measures such as electroencephalography and near-infrared spectroscopy
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. Required Qualifications · PhD in Geophysics · Experience with land seismic acquisition, DAS, and time-lapse monitoring · Proficient in computer programming · Extensive publication and presentation record
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thinking skills, and ability to work as part of a multidisciplinary team. High computer proficiency, including basic MS Office, MS Access, graphics, and database software. Basic project management and
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, or free speech. Candidates should hold a PhD in a field such as History, Political Science, Public Policy, Criminology, Gender and Women’s Studies, Black Studies, Latino Studies, Ethnic Studies, Sociology
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Start Date: Immediately ---- Position Duration: Expected to Continue Until Apr 20, 2026 ---- Location: AUSTIN, TX ---- Job Details: General Notes PhD must have been received within the past 3 years
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) mentored training program for pharmacists with a strong interest in pursuing careers in pharmacy education. The program will provide extensive experiences in classroom and experiential teaching, educational