107 machine-learning "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" Postdoctoral positions
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greatest challenges; and develop innovative partnerships to produce master learners across the lifespan. To learn more about ASU, visit http://www.asu.edu . Essential Duties Design and implement digital
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at the interface of biostatistics, machine learning, and biomedical data science. This mentored postdoctoral position is designed to support the development of an independent research trajectory in methodological
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sciences, including technologies that are driving the generation of massive datasets, the exponential growth of publicly available data, and revolutionary approaches in statistics, machine learning, and
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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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multi-omic sequencing, network biology, and machine learning to identify actionable biomarkers and therapeutic vulnerabilities. The successful candidate will work at the interface of computational
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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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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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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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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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