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Field
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of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts are supported by
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silico work, including sequence design, data analysis, experimental planning, and writing publications. The postdoc will work closely with the PI and a PhD student. An ideal candidate must hold, or be
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demonstrated independent thinking and problem-solving abilities. Experience using large databases is preferred, and genetic data analysis skills are desirable. Required Qualifications PhD or MD in Public Health
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. Responsibilities: This postdoctoral position will involve participating in existing studies, leading independent research, as well as conducting analysis of existing data, which can range broadly from psychosocial
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 4 hours ago
to scientific data analysis. Development of secondary projects which foster collaborations with other researchers both at NASA Ames and externally is also encouraged. Field of Science: Earth Science Advisors
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/bioinformatics, and data science. Work Performed · Work in highly collaborative inter-disciplinary environment with clinicians, econometricians, statisticians, and data scientists · Lead statistical analysis
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Internal Number: 6397400 Postdoctoral Scholar - Voting Law/Data Analysis- Goldman School of Public Policy Position overview Position title: POSTDOC-EMPLOYEE Salary range: The UC postdoc salary scales set the
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motivated and independent researcher with: a PhD in marine biology, ecology, or a related field. experience in marine mammal research, particularly in data collection and (statistical) analysis. strong
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. Evaluating environmental sustainability via Life Cycle Assessment (LCA) and techno-economic analysis. Contributing to the integration of vanadium flows within a circular economy model for battery materials
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. The postdoctoral fellow will work with a team of investigators from institutions across the Northeast on a NIH-funded project on phenotyping obesity using behavioral, psychological, familial, and genetic data