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Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to contribute to our lab’s mission of aligning machine learning (ML) models with algorithmic reasoning tasks. Our goal is to
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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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or translational research optimizing advanced water treatment processes (e.g., reverse osmosis) to reduce costs and emissions while providing safe resilient supply. They should have documented experience with a
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this project to propel discovery of how recent advances in AI can promote healthy aging and longevity around the world. The general aim of the work is to study how new LLM-type modeling architectures can be
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study of ECE and policy impacts. Strong data analytical skills using advanced statistical methods (such as mixed effect models, multilevel modeling, structural equation models, longitudinal modeling, etc
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. Mentorship Structure The postdoctoral research scholar will be supervised and primarily mentored by Professor Mykel Kochenderfer, Faculty Co-Director of the Stanford Center for AI Safety, and Associate
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experiences. The Fletcher Lab at Stanford University uses computational systems modeling to advance resilient and equitable water resources management for an uncertain future. Current research topics in the lab
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groups. The fellow will also help with grant writing to get practiced in the skill of academic fundraising that will be vital for their future career. Mentorship Structure The postdoc will be a member of
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environmental stimulants. We employ an interdisciplinary approach to probe, model, and predict how signaling network dynamics translate extracellular cues sensed by GPCRs into specific phenotypic outputs
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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