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University is seeking a postdoctoral scholar to support and lead innovative research at the intersection of rheumatology, pain science, and data science. The successful candidate will work on a portfolio of
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chain network analysis and geospatial modeling. The successful candidate will have strong data science skills, including experience working with large, complex data from varied sources, and machine
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expertise in Neuropixels or other large-scale in vivo electrophysiology techniques. An expert neural data analyst may be considered even with minimal animal experience. Required Application Materials: Cover
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care actually happens, and how it can be made better. This is a role for someone who’s excited to work with big, messy, real-world data — and who wants to do more than just build models. We’re looking
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model fairness and model generalizability across multi-institutional electronic health records databases. The researcher will have access to the real-world EHR data from almost 20 sites across
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verbal. Preferred Qualifications: Experience combining large clinical and research data sets. Interested and comfortable working with pediatric patients with special needs. A track record of publications
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of the post-doc is to study how innovations in AI, especially adaptation of Large Language Models (LLMs) architectures for time-series data, can be used in study of aging, health span, and longevity
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include a PhD in Computer Science, Artificial Intelligence, Natural Language Processing, Human-Computer Interaction, or a closely related field. Candidates should have demonstrated expertise in Large
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, submit the required application materials, or find out more information, please contact Dr. Brian Kim (kimjb at stanford edu). The position will remain open until filled. Does this position pay above the
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technology and b) large-scale data collection in a diverse sample spanning over 250 schools across 30 states to answer three significant questions regarding the mechanisms of word reading difficulties such as