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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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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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large-scale optimization and theoretical combinatorial optimization, algorithmic reasoning remains a significant challenge for artificial intelligence. Our lab’s research is driven by the observation
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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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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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, 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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different disciplines and mentors Stanford Departments and Centers: Med: Biomedical Informatics Research (BMIR) Biomedical Data Sciences Postdoc Appointment Term: 1 year minimum with the option to extend
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from real world longitudinal data on management and health outcomes for children with mental health conditions. Methods have included deep learning, large language models (LLM), generative AI models (Gen
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on hotspot detection. This modeling work is well supported by large-scale primary datasets, including survey-based, parasitological, serologic, and genomic data. Relevant methodologies include mechanistic