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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 18 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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University. This research opportunity will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including bulk and single
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
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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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analytical many-body calculations Expertise in one or more of the following: computational methods, software and algorithm development, high-performance computing, and data analysis Proficiency in relevant
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work
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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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Associate on the track of Smart Integrative Energy Systems will participate in the research efforts of developing systems integration, analysis, design, control, and/or optimization models and algorithms
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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research