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familiarity with AFNI are strongly preferred. Knowledge, Skills and Abilities: General understanding of scientific theory and methods. General computer skills and ability to quickly learn and master computer
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statistical analysis, data reports and management. The division is eager to determine novel approaches towards quantifying movement through wearable and video technology and develop novel methods of analyzing
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of research data analyses, and associated academic presentation and publication of research methods. Experience in both conducting data analysis and supervising the work of data analysts when available
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. Knowledge, Skills and Abilities: General understanding of scientific theory and methods. General computer skills and ability to quickly learn and master programs. Ability to work under deadlines with general
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expertise on most or all of the systems and work independently with little assistance from a lead or supervisor. May also develop new methods or procedures for carrying out unique assignments; and give
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research projects in lab and/or field based on knowledge of scientific theory. Independently conduct experiments; maintain detailed records of experiments and outcomes. Apply the theories and methods of a
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Biomedical Data Science, Research IT, and Research Informatics Center. The incumbent will work with a combination of structured and unstructured (text, imaging) data from several sources, including Stanford's
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of experiments and outcomes. Apply the theories and methods of a life science discipline to interpret and perform analyses of experiment results; offer suggestions regarding modifications to procedures and
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; maintain detailed records of experiments and outcomes. * Apply the theories and methods of a life science discipline to interpret and perform analyses of experiment results; offer suggestions regarding
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to developing computational methods to better understand individual differences in brain functioning in healthy and patient populations. For more information about the lab, please visit http://bdl.stanford.edu