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Field
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. Experience with conjoint analysis (e.g., discrete choice experiments and best-worst scaling), experience with large scale survey and retail data, portfolio in substance use or other health-related areas, and
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(45%) • Support statistical analyses of large data sets from an epidemiological cohort, investigating associations with birth outcomes using both targeted and nontargeted data (25%) • Support
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in the analysis and interpretation of single-cell multi-omic data. This role will contribute to cutting-edge research by integrating statistical and computational approaches to address complex
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 15 days ago
. Description: Under the guidance of a mentor, the participant will engage in a NASA-aligned research project advancing data-driven approaches to characterize wildfire risk and post-fire vegetation recovery
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at the Large Hadron Collider (LHC). The successful candidate will contribute to a broad research program that includes physics analysis, detector performance studies, experiment operations, and upgrade
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, spatial statistics, machine learning approaches, large ecological datasets Previous experience with passive acoustic monitoring and/or eBird data Familiarity with ecology and/or ornithology Demonstrated
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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systems, or large-scale data pipelines. Evidence of open-source software development, reproducible research practices, and successful interdisciplinary collaboration. Demonstrated record of scholarly
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 15 days ago
for large-scale ice-sheet models such as ISSM, and provide a pathway toward increasingly integrated model–observation frameworks, including data assimilation. References: E Rignot, S Jacobs, J Mouginot, B
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of real-world evidence using large real-world data sources, including data from the ongoing prospective cohort study (DREAM cohort). The successful candidate will have expertise in epidemiologic research