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Posting Summary Logo Posting Number RTF00210PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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Candidates with experience in flow cytometry, single-cell analysis, quantitative imaging, and routine use of R or Python for data analysis and visualization will be especially competitive. The ideal candidate
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visualizations that advance research and science communication. Examples of current and past projects can be found at http://infographics.uoregon.edu/. Research positions are funding contingent, part-time, limited
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I, Psychology Posting Number req26708 Department Psychology Department Website Link https://psychology.arizona.edu/ Location Tucson Campus Address Tucson, AZ USA Position Highlights The Postdoctoral
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lipids to visualize and measure inter-organelle trafficking and metabolism of lipids in single cells. This position requires knowledge of the endomembrane system and strong skills in molecular biology and
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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization
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production and visual sensitivity, a web-based LCOE calculator, and a multi-criteria decision analysis (MCDA) methodology for comparing candidate demonstrator sites. The project directly supports Action 3 and
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 13 hours ago
. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Eligibility is currently open to: U.S. Citizens; U.S. Lawful Permanent Residents (LPR); Foreign
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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seeking a highly motivated Postdoctoral Researcher to join the Machine Learning cluster. The position is part of a research project investigating how visual foundation models can efficiently acquire new