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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 18 hours ago
thermal conditions, and geophysical modeling. A wide range of research is possible, from the analysis of existing lunar datasets, to the development of new modeling approaches, and to simulations of
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supervised models on downstream regression tasks. A key challenge lies in effectively modeling the temporal nature of EO data. Since downstream applications require varying temporal resolutions and sequence
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Doctoral student in developing PEM fuel cells by multiscale modeling Reference number REF 2026-0344 Do you want to use advanced simulation methods to understand and improve the performance of fuel
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start date and demonstrate outstanding research in computational physics for modeling of complex physical phenomena. Additional Qualifications Candidates must have experience in carrying out large-scale
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system modeling Basic understanding of graph neural networks, foundation models, data-driven modeling, or physics-informed machine learning Good programming skills in Python Experience with machine
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, (qi.ucsd.edu) is conducting an open search for a Research Scientist (Associate or Full level) in Remote Sensing, Mapping, Modeling and Multimodal digital documentation and digital twin synthesis. QI
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interface of reactive molecular simulations and data-driven modeling. The successful candidate will develop and apply data-driven methods for chemical discovery and molecular design. These methods include
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Your Job Our Modeling and Simulation Group offers an interdisciplinary and agile research environment within a dynamic and diverse group. The project is an excellent example for research at the
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in theoretical and computational chemistry whose research lies at the interface of reactive molecular simulations and data-driven modeling. The successful candidate will develop and apply data-driven
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modeling and the emergence of low-dimensional dynamics from high-dimensional coupled systems. Complementary directions include data-driven modeling of circadian and social-network time series, and