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Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
missions (e.g., Surface Biology and Geology - SBG). This could involve advancing atmospheric correction, dimensionality reduction, or machine learning approaches for handling big data in order to improve
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senior research position to work on projects related to computational analysis of mass spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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related to learning engineering and AI in education, working with a team of postdoctoral researchers, PhD students, and Master’s/undergraduate researchers across multiple universities and organizations
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) Cleaning and managing large datasets from administrative data sources or online learning platforms Causal machine learning (e.g., double/debiased machine learning (DML), causal forests, generic ML) Learning
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Posting Details Position Details Title Postdoctoral Fellow in Biostatistics and Health Data Science Specific Title Appointment Type Postdoctoral Fellow Department IUSM - Biostatistics Campus IU
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Description The Teamcore Group (https://teamcore.seas.harvard.edu) at the John A. Paulson School of Engineering and Applied Sciences (SEAS) at Harvard University seeks postdoctoral fellows to work on AI
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learning, uncovering the modes of learning available to linear and nonlinear systems, their expressiveness and capacity, and the physical imprints of learned tasks. The postdoctoral researcher will
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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods