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emulators for accelerated forward modeling Advanced data-intensive machine learning and AI techniques for survey analysis Applications to major international surveys, including LSST (Rubin Observatory
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computational, theoretical and/or observational projects, to develop and deploy cutting-edge machine-learning and AI methods for astrophysics and cosmology, enabling precision tests of fundamental physics with
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matter observatory. Main responsibilities The postdoctoral candidate is expected to focus on statistical data analysis including machine learning, Monte Carlo simulations, operations and calibration
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The Department of Ecology, Environment and Plant Sciences invites applications for postdoktoral fellow for the project “Harnessing evolutionary transitions, machine learning, and genomics to decode pollen