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materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
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LEARNING TECHNOLOGIES ACCESSIBILTY SPECIALIST - (T232321) Description The University of Alabama at Birmingham (UAB) seeks a highly skilled individual in digital accessibility in course materials to
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administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate: 01.02.2026 | Working hours
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The ideal candidate applies machine learning and big data techniques to important questions in economics, combining advanced computational methods with sound economic theory to uncover insights
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The role will engage in cutting-edge translational research that develops computational models for predicting outcomes in cardiac diseases. This includes a machine learning model to rule out heart
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want to hear from you! Your Job: Work on a wide range of computer vision and machine learning methods and applications focusing on the aspects outlined above, inspired by the needs of societally relevant
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Details Title Postdoctoral Fellowships in Networking Support for Machine Learning School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Science Position
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year of relevant research experiences in similar research role / technical hands-on experience in AI and machine learning Familiarity with large-language models Good written and oral communication skills
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particular focus on the application of machine learning and big data methods. The position involves producing high-quality, peer-reviewed research suitable for publication in leading international journals, as
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 10 hours ago
). Revisiting Ionosphere-Thermosphere Responses to Solar Wind Driving in Superstorms of November 2003 and 2004. J. Geophys. Res., 122. https://doi.org/10.1002/2017JA024542 . 2. McGranaghan, R. M., A. J. Mannucci