106 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" Postdoctoral positions
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and machine learning. Dr. Liu's research interests lie in modeling the rapidly-accumulating big data (e.g., muti-omics) in biology and medicine for precision medicine via a variety of statistical and
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Scholar Salary range: A reasonable salary range estimate for this position is $66,737 - $80,034 based on level experience. The posted UC academic salary scales (https://www.ucop.edu/academic-personnel
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The Department of Biostatistics at the University of Washington has an outstanding opportunity for a postdoctoral scholar. The postdoctoral scholar will develop statistical machine learning and artificial
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 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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https://pubs.acs.org/doi/full/10.1021/acssuschemeng.5c0419 The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and
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eligible for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https
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to recruit 100 new tenure-system faculty to strengthen its research enterprise and leadership in key academic areas. Learn more at https://www.uta.edu/administration/president/strategic-plan/rise100 . This is
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to efficiently collect/organize/analyze data and initiate new/modified procedures/techniques based on the latest developments are expected. Certificates/Credentials/Licenses n/a Computer Skills General office
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Organization U.S. Department of Energy (DOE) Reference Code DOE-Scholars-2026-EM How to Apply Click on Apply below to start your application. Application Deadline 3/31/2026 8:00:00 AM Eastern Time
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sciences.Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Proposals that include deep or machine learning