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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 hours ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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. Specific responsibilities include, but are not limited to, the following: Develop the core tensor network algorithm for full RIXS cross-section simulations. Benchmark simulation results against ED codes
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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, transfer learning, federated learning, data integration, algorithmic fairness, survival analysis, and methods for heterogeneous and multi-source data. Training Environment and Career Development
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 2 months ago
climate system and the effect of ice-atmosphere feedbacks (e.g., ice albedo feedback and cloud feedback) on sea ice evolution. Developing radiative transfer algorithms to improve both the physical
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and