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help psychometrics become a more data-driven discipline. Our labs are combining in this project to propel discovery about development of children’s cognitive abilities in heterogeneous contexts
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include processing of large data sets including RNAseq. Apply the theories and methods of a life science discipline to interpret and perform analyses of experiment results; offer suggestions regarding
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substantial component of the work focuses on large scale empirical research in international macroeconomics and finance. The Global Capital Allocation Project (GCAP) Lab mixes data, economic theory, and
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into tangible products. Critically, this work will generate a large open-source dataset of child-created games that can inform future designs of educational games and AI algorithms. The postdoctoral fellow will
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brain science, learning research, data, and technology, the Stanford Accelerator for Learning works to tackle the most pressing challenges in education today, turning new discoveries into real-world
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external) provides algorithmic and data-driven solutions providing fairer access to public educational resources. The lab enables school districts to optimize large-scale equitable resource allocation
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required. Good laboratory practices are critical. Ability to undertake and complete large research projects and communicate findings to the field as demonstrated by first author publications in respected
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) natural language processing and large language models, (4) generative AI, (5) dynamic risk prediction modeling for high-dimensional survival data using longitudinal features, and (6) machine learning and
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projects focused on emergency medicine health services. • Utilize advanced quantitative methods to analyze large healthcare datasets, including Medicare and Medicaid claims data. • Publish findings in high
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traditional 2D geological maps into complex 3D structures by harnessing state-of-the-art data science and machine learning techniques at large scales. The research will use geophysical, geochemical, and