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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: Computational Science and Engineering / AI/ Machine Learning , Artificial Intelligence , Data Sciences , Machine Learning Machine Learning Theoretical Physics / Statistical physics Complex
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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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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
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dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You
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. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create
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. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create
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simulations. Close collaboration with theoretical and experimental teams at Stockholm, Mainz, and Tübingen. Assistance in the supervision of PhD and Master’s students in the lab. Contribution to grant reports
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the research area described above. Applicants are expected to hold (or be close to completing) a PhD in political science, or another field relevant to the project. A specialization in gender and politics
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people, including 35 PhD students and 15 postdocs, in 25 research groups, work at DEEP. More information about us, please visit: the Department of Ecology, Environment and Plant Sciences . Project