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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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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups
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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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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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scientific research on computational linguistics, machine learning, practical applications of human language technology, and interdisciplinary work in computational social science and cognitive science. The
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and other machine learning models (especially neural network models, time-series models) and coding in python and R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. Responsibilities: Conduct research on the mechanisms underlying
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for healthcare. The Alsentzer Lab is an interdisciplinary research group in the Department of Biomedical Data Science at Stanford University. Our mission is to leverage machine learning (ML) and natural
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external) How to Submit Application Materials: To begin the application process, please send an email using the subject line “Postdoctoral Position in Machine Learning for Advancing Mental Health” to Tina
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focus on machine learning in the Stanford Center Cancer Cell Therapy at Stanford University School of Medicine. We seek a highly creative and motivated scientist to perform cutting-edge computational