3,469 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" positions
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qualified candidates to apply, including members of the military and veterans, and persons with disabilities. WSU employs only U.S. citizens and lawfully authorized non-U.S. citizens. All new employees must
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series is represented by the United Auto Workers of America (UAW) and information about the collective bargaining agreement is available on the UCOP website. https://ucnet.universityofcalifornia.edu/labor
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; more than a quarter of a million alumni in 160 countries; and a student population from all 50 U.S. states and 123 countries. For more information, please visit http://www.syracuse.edu. About the Syracuse
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 8 hours ago
missions (e.g., Surface Biology and Geology - SBG). This could involve advancing atmospheric correction, dimensionality reduction, or machine learning approaches for handling big data in order to improve
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. The salary range for this position is $72.96 - $90.91 (Hourly Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu
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sponsorship. About Department/College/Area/ Campus - https://tricities.wsu.edu/academic-resources/ Area/College: WSU Tri-Cities Department Name: Learning Commons & Student Services Location: Richland, WA 99354
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, including members of the military and veterans, and persons with disabilities. WSU employs only U.S. citizens and lawfully authorized non-U.S. citizens. All new employees must show employment eligibility
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(https://www.ucop.edu/academic-personnel-programs/compensation/2025-26-academic-salary-scales.html ) set the minimum pay determined by rank and/or step at appointment. See the following table for
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machine learning. There are currently 54 academic staff and about 105 research personnel in the Department. Please visit the website at https://www.polyu.edu.hk/ama for more information about the
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/10.1016/j.xcrp.2022.101112 and https://doi.org/10.1080/08940886.2022.2114716 key words synchrotron radiation; X-ray Absorption Spectroscopy, machine learning, artificial analysis, autonomous experimentation