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, aligning academic research to practical challenges in education. Our mission is to inspire a virtuous cycle between research and practice, supporting equity in education through the open dissemination
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that, in practice, there is often a wealth of data specific to the application domain that can be leveraged to optimize algorithmic performance. For instance, the scheduling problems that an electric grid
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methods to improve prediction model generalizability, model fairness, and generalizability of fairness across different clinical sites. The researcher will have the opportunity to use machine learning and
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in NHLBI, NIAMS, and NICHD-funded studies, focusing on the intersection of perioperative pain, sleep, and opioid use. Fellows will be exposed to longitudinal observational, interventional, and
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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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are currently supporting hundreds of schools around the United States (and abroad) with a more efficient and precise approach to assessment. Through this research practice partnership model we are also able
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Professionals certification is preferred. PHYSICAL REQUIREMENTS*: Frequently stand, walk, twist, bend, stoop, squat and use fine light/fine grasping. Occasionally sit, reach above shoulders, perform desk based
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research on the implications of physician practice against challenging issues in healthcare. Specific near-term (funded) projects include: (1) The Stanford Center for Asian Health Research and Education
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will use a combination of scRNAseq, spatial transcriptomics, and highly-multiplexed imaging to understand how human macrophages respond to the early stages of cancer development. They will be a part of
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use of cutting-edge informatics to prioritize causal variants for validation using functional genomics, including CRISPR. The overall goals of this work are to define novel disease targets and context