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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 7 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
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Robust machine learning using information theoretic approaches for damage detection in complex machines (C3.5-ELE-Esnaola) School of Electrical and Electronic Engineering PhD Research Project
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world. We look forward to receiving your application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is
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hardware configuration, conduct Hardware/Software requirement analysis, embed cybersecurity, develop software development, and embed Data Science and Machine Learning capabilities. Specifically
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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the production of polymer latexes that involves a complex, heterogeneous polymerization system and leads to polymers with a diverse range of structures. This project looks to use machine learning to better target
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Professor (tenure-track) and/or Associate Professor levels with a focus on Data Science and Machine Learning. The expected starting date is summer 2026, with room for flexibility. Depending on the profile
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16.01.2026 – 2:00 PM – Resolution of appeals 16.01.2026 – 4:00 PM – Final results Additional comments We are seeking a PhD student in Machine Learning for EO Image Processing to join our team, who will be
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2,900 work in 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
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. The Department of Chemistry and Materials Science is looking for: A Doctoral Researcher (PhD student) in Machine Learning for Surface Structures The Data-driven Atomistic Simulation (DAS) group, led by