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Information Technology. A full-time postdoctoral traineeship on an NIH funded T32 training grant entitled “Women’s Health and Intersectionality Using Data Science and Health Information Technology”. The purpose
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contribute to the excellence of our academic community. Postdoctoral Fellow – Sleep Medicine - A highly motivated Postdoctoral Fellow is sought to join the research group of Dr. Victoria Pak on an NIH R01
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and growing academic surgical research program. This fellowship is designed to provide comprehensive training in clinical research methodology, outcomes research, clinical trials, and translational
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Ph.D. in Computer Science, Data Science, Economics, Engineering, or related field Excellent scientific written and oral skills Experience in developing state-of-the-art optimization and prediction models
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necessary to advance basic and/or translational research programs. Responsible for working with experimental platforms specific to the hiring Program. Duties will include but are not limited to experimental
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postdoctoral fellowship and/or has prior work experience in pediatric chronic pain is strongly preferred. Opportunities for development of groups or other clinical programming is available and supported by
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. Dr. Zhang received her Ph.D. in Cell Biology from Yale University and her M.S. in Computer Science from Stanford University. She finished her postdoctoral training at the University of California, San
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doctoral program in psychology, completion of an APA-accredited internship, and completion of postdoctoral fellowship is preferred. Ideal candidates will have clinical training/background in applied behavior
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infectious disease epidemiology and vaccine science at a moment of exceptional importance for public health. This role will support rigorous, policy-relevant research including vaccine effectiveness evaluation
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, Biostatistics, Computer Science, Economics, or a related quantitative field. Strong background in causal inference, statistical methodology, or machine learning. Experience with nonparametric or semiparametric