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it; as well as have theoretical skills including algorithm implementation/development and data visualization. Experience and interests include designing machine learning pipelines, building web
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data sets. Develop and implement algorithms for data processing and interpretation. Collaborate with clinicians and researchers to design studies and analyze results. Present findings to both scientific
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communicate by flexibly reasoning about what other agents know and want. Recently, we have been exploring how this framework of inferential social learning can be applied to develop socially intelligent
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to uncover vulnerabilities in complex systems, bridging gaps between traditional testing techniques and emerging security threats. This work involves developing novel techniques, algorithms, and software
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medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user interfaces to power automated
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experiences. This is a one year position with the option to renew for a second year, pending funding. The Stanford Impact Lab on Equitable Access to Education (link is external) provides algorithmic and data
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developed algorithms to real-world datasets and generate valuable biological insights. Perform integrative analyses of multidimensional datasets within the context of cancer and Alzheimer’s disease
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omics to advance biological and clinical discoveries and develop next-generation theragnostics. The postdoctoral fellows will mainly focus on (1) creating novel computational algorithms to analyze and
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the complexities of the human regulome through advanced cell-free DNA profiling and developing cutting-edge computational algorithms and molecular profiling techniques. Our research focuses on early cancer detection
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University. This research opportunity will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA