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Health, Environmental Health, Biological Sciences, Biostatistics, Data Science, preferably with relevant experience. Prior experience with machine learning is a plus. Recruitment is open immediately and
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in this position will conduct/lead applied as well as fundamental research in physics-informed Artificial Intelligence (AI) and Machine Learning (ML) methodologies enabling digital twin functionalities
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About the Opportunity About the Institute Do you want to be part of an exciting new Institute focused on combining human and machine intelligence into working AI solutions? We are launching a
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2012, FSPH is fully accredited by CEPH and offers three PhD programs, an MPH with five concentrations, an MHA program, several graduate degrees, and BS degrees in Health Services Management and Public
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of analyzing large-scale population data. Experiences working with electronic health records (desirable). Understanding of clinical informatics approaches (e.g., machine learning, Bayesian statistics) and
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microphysics, severe storms, and mesoscale meteorology; data assimilation, machine learning, and causal discovery; education and human resources; global biogeochemical cycles and ecosystems; and radiation and
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Details Title Postdoctoral Fellow in Deep Learning Theory and/or Theoretical Neuroscience School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position
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-FATES model using: Snow cover Flux tower data The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently
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of Engineering and Applied Sciences. The fellow will design and run human experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal