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external) is reimagining the experimental neuroscience pipeline with big data and AI at its core. A central goal of the project is to build a foundation model of the visual brain—a “digital twin” that
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disease, including AI-powered tools and new statistical techniques that leverage large datasets, heavy computational capabilities, and/or a robust understanding of biological systems to provide unique
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, publications and experience presenting data at national and international meetings Names and addresses of 3 academic references. One of these must be your main PhD supervisor. Stanford is an equal opportunity
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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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primary research areas: 1) statistical inference in high-dimensional and large-scale testing scenarios; 2) the development of novel model architectures for large-scale proteomics data; and 3) causal
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to function studies in the context of human genetics of complex traits and generating large data sets for collaborative science. The PI is committed to training and mentoring and will provide exceptional
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/artificial intelligence; (f) health policy, health economics, and decision science; (g) and implementation science Eligibility The ideal candidate will have an MD/DO, PhD, or ScD. PhDs/ScDs may be in clinical
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spatial and temporal monitoring results in enormous volumes of data, necessitating the management of large datasets and communication of extensive (and expensive) data packets. These factors necessitate new
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population-level outcomes. Utilize advanced quantitative methods to analyze large healthcare datasets, including Medicare and Medicaid claims (MedPAR, Outpatient, Carrier, TAF). Develop reproducible code and
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urgent questions of practical relevance and to design studies to test pragmatic solutions, analyze data, and disseminate findings and implications. The program will also provide fellows with training in