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on developing and applying cutting-edge machine learning, data science, and computational methods to study the large-scale structure of the Universe using cosmological survey data. Postdoctoral researchers at OKC
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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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Areas: Theoretical Physics / Statistical physics Machine Learning Computational Science and Engineering / AI/ Machine Learning , Artificial Intelligence , Data Sciences , Machine Learning Complex
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The Department of Biochemistry and Biophysics. SciLifeLab (SciLifeLab ) is a national center for molecular biosciences with focus on health and environmental research. The center combines frontline technical expertise with advanced knowledge of translational medicine and molecular bioscience....
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innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. More specifically, at NRM this research will be
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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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Jan 2026 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 40 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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- 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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Application Deadline 23 Sep 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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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