32 Statistics positions at Stanford University in united-states-of-America in United States
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interdisciplinary collaborative statistics unit in the Biomedical Informatics Research Division within the Department of Medicine at Stanford University. The mission of Stanford's Quantitative Sciences Unit is to
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data, and disseminate findings. Independently work with investigators on proposal or grant development. Take the statistical lead on studies. Oversee biostatisticians in the lower levels on data cleaning
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involving researchers from oncology, radiobiology, computer science, genetics, statistics, and basic/translational clinical research. Our research couples state-of-the-art computational and experimental
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into individual research teams. Duties include: Design study. Develop and implement protocol for quality control. Create analytic files with detailed documentation. Select appropriate statistical tools
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involving researchers from oncology, radiobiology, computer science, genetics, statistics, and basic/translational clinical research. Our research couples state-of-the-art computational and experimental
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collaborative approach where QSU members become fully integrated into individual research teams. EDUCATION & EXPERIENCE (REQUIRED): Master’s degree in biostatistics, statistics or related field and three years
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collaborative approach where QSU members become fully integrated into individual research teams. EDUCATION & EXPERIENCE (REQUIRED): Master’s degree in biostatistics, statistics or related field and three years
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The Quantitative Sciences Unit (QSU) is an interdisciplinary collaborative statistics unit in the Biomedical Informatics Research Division within the Department of Medicine at Stanford University
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The Quantitative Sciences Unit (QSU) is an interdisciplinary collaborative statistics unit in the Biomedical Informatics Research Division within the Department of Medicine at Stanford University
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the CMQCC Maternal Data Center (MDC) software and other data tools such as Tableau and R/Python or Stata statistical tools/models. Experience with forecasting, trend analysis, and predictive modeling using