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given to candidates studying early China using analytical methods such as zooarchaeology, paleobotany, ceramic analysis, and lithic analysis. The successful candidate will be expected to: Teach one course
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for Biomedical Informatics Research at Stanford University. This position emphasizes conducting real-world evidence studies using various causal inference methods (e.g., target trial emulation) to examine
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systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user
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(community interventions, community-based participatory research, meta-analysis and bias in research, RCT methods, causal interference, mathematical modeling, and econometrics) Policy research related
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theoretical modeling, remote imaging, and direct observation. Our research has both fundamental and applied elements. The successful candidate will demonstrate exceptional academic achievement and promise, and
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factor. However, non-U.S. scholars must be eligible for a J-1 visa, (with sponsorship from Stanford). Exceptions must be discussed with program administration. Email energy_postdoctoral_fellowship
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(link is external) ); Monocle 2 and 3 for unsupervised pseudotemporal trajectories inference: Qiu et al., Nature Methods, 2017a/b (https://doi.org/10.1038/nmeth.4150 (link is external) , https://doi.org
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and make sue of some of the new methods we have developed, with a particular focus on human immunology and diseases. Required Qualifications: Ph.D in some area of immunology Enjoy collaborative work and
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. Requirements are a Ph.D. in immunology, publications and a willingness to learn and make use of some of the new methods we have developed, with a particular focus on human immunology and diseases. Required
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psychological change processes. The lab develops and uses novel longitudinal methods to articulate and examine how individuals change over time (and with age), and how various individual characteristics