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Field
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, high-performance computing (HPC), dimensionality reduction, and machine learning. Your responsibilities will include publishing research in leading conferences and journals, contributing to grant
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scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and machine learning
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applied to environmental chemical exposures, type 2 diabetes, kidney disease, and cancer. The ideal candidate will be open-minded, eager to learn, and enthusiastic about engaging in our multidisciplinary
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information here ! PURPOSE We are seeking a highly motivated researcher with a strong background and interest in machine learning and artificial intelligence. The position focuses on the development and
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abilities and values to serve our regional, national, international, and military communities, and create a nurturing and learning environment for students, faculty, and staff. Primary Purpose of the Position
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candidate with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental
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projects in AI-driven GNC for space robotics systems, leveraging both classical optimization techniques and modern machine learning methods. Lead and support research projects in AI-driven solutions for SDA
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research projects in AI-driven GNC for space robotics systems, leveraging both classical optimization techniques and modern machine learning methods. Lead and support research projects in AI-driven solutions
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machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 41 minutes ago
on the principle that by integrating high-resolution Earth observation (EO) data from NASA with state-of-the-art machine learning, we can produce a more accurate, dynamic, and actionable measure of wildfire risk