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Qualifications: PhD (or equivalent) in Biostatistics, Epidemiology, or a closely-related quantitative field (by start date) Strong training in causal inference methods Experience working with large-scale
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multimodal MRI data from children with NF1 using the Siemens Cima X scanner at Stanford’s Lucas Center for Imaging. The position sits at the intersection of advanced neuroimaging methods, longitudinal clinical
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mentoring access to have breadth of talented affiliated faculty members and consultants (e.g., Mirko Uljarevic, MD, PhD). Didactic and hands-on training in many theoretical and applied areas of research will
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analysis of multiple disease-specific datasets and contribute to the development of novel methodologies in this space. The ideal applicant will have a strong background in bioinformatics methods and a keen
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with researchers both at Stanford and the U.S. Census Bureau. The position is open to recent graduates of PhD programs in economics, statistics, sociology or related data science fields, preferably with
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methods from econometrics. The project extends the work of Proctor and colleagues (Nature, 2018, https://doi.org/10.1038/s41586-018-0417-3 (link is external) ) to other impact variables. Impact variables
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for recent MD and PhD graduates who are passionate about leveraging computational methods to transform trauma and acute care surgery. Fellows will work at the intersection of clinical medicine, data
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Obradović is seeking a postdoctoral scholar to join her research group in the Graduate School of Education at Stanford University. We encourage applications from individuals from a broad diversity of PhD
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and policy methods to real-world questions in acute and population health. Key Responsibilities Conduct and manage research projects focused on emergency medicine health services, care transitions, and
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the resulting data from the experiments. Required Qualifications: Candidate must have a strong quantitative background, with a PhD in computational biology, bioinformatics or related field including