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are looking for a postdoctoral researcher to contribute to cutting-edge research at the intersection of language models, data, and reasoning. The postdoc will be part of Stanford Language, Data, and Reasoning
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journals and oral conference presentations. Proficiency in culturing and maintaining mammalian cells, particularly mouse cell lines and organoids- preferred Experience with mouse models of disease
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employ advanced analytical methods in large databases, which include claims data and electronic health record data in conventional structures and in common data models. Our research group prioritizes a
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Qualifications: Ph.D. in Molecular Biology, Virology, Cell Biology, bioengineering or related field. Strong background in AAV biology, including hands-on experience with AAV vectors in cellular models
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, mouse models, standard molecular biology and biochemistry, FACS/flow-cytometry analysis, genetic manipulation of cells (e.g., CRISPR), and microscopy. Candidates with experience in computational analysis
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diseases, in particular glaucoma. This position involves the application of optic nerve crush and other models of retinal ganglion cell injury to study signaling pathways in vivo. This position will involve
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, utilizing various AI methods, predictive modeling approaches, and large language models. Specific areas of interest include but are not limited to (1) electronic health records, (2) causal inference, (3
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Center for Biomedical Informatics Research at Stanford University. This position emphasizes evaluating various cancer screening strategies by developing and applying microsimulation models for decision
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, electrophysiology, rodent microsurgery and animal models of glaucoma and other ocular diseases. The postdoctoral fellow will be joining a young lab with exciting opportunities to make discoveries that intersect
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the development of clinical deep learning and other machine learning models to enable improved diagnosis, prognostication, and prediction of treatment response for bladder cancer, specifically related to endoscopic