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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | about 2 hours ago
. Description: The Principal Component-based Radiative Transfer Model (PCRTM) group at NASA Langley Research Center has developed innovative radiative transfer models and associated retrieval algorithms
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new opening for a postdoctoral scholar to develop cutting-edge mathematics and algorithms to analyze complex data from Department of Energy (DOE) experimental facilities. This role involves research and
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unsupervised techniques, time-series modeling, and clustering algorithms. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple
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developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms
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experimental evaluation of algorithms. Prepare and publish research findings in peer-reviewed journals and present results at conferences. Assist in the mentorship of graduate and undergraduate students
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opportunity to develop cutting-edge AI-enhanced algorithms for analyzing transposable elements (TEs) in cancer epigenomics, with direct translational applications to precision oncology and immunotherapy
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guiding new mathematical and algorithmic developments. A Ph.D. in mathematics, physics, computer science, or a closely related area is required. To be considered for the initial review, with an intended
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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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will have the opportunity to develop innovative algorithms and models that integrate multiple data modalities, collaborate with industry partners, and contribute to high-impact publications. Job
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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