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: Within this international project, TU Delft will develop a machine learning-based forward operator to enable the assimilation of SAR imagery into the crop growth model. You will: Process SAR imagery over
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and apply AI and machine learning methods for signal processing, image analysis, data fusion, and prediction; · build physics-informed and hybrid AI models that combine geophysical knowledge with data
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 20 days ago
); and propagation of physics-traceable soil moisture uncertainty into precipitation estimates. Machine learning approaches are welcome as tools within this framework — for example, learning the space–time
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 22 hours ago
of Yuhua Duan. This project will be hosted at the NETL Pittsburgh, PA campus. Although material modeling with artificial Intelligence/machine learning (AI/ML) applications and experimental instrumental
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collaborates with leading tech firms and advance research in robotics, AI, battery technology, aerospace, water systems and sustainable energy. Learn more at unr.edu/engineering Exempt Yes Full-Time Equivalent
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, battery technology, aerospace, water systems and sustainable energy. Learn more at unr.edu/engineering Exempt Yes Full-Time Equivalent 100.0% Required Attachment(s) Please note, once you submit your
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desirable. Experience with artificial intelligence, machine learning, reinforcement learning, or optimization techniques applied to autonomous systems is considered an asset. Proficiency in relevant
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stimulation (TMS), and transcranial direct current stimulation (tDCS) through interdisciplinary collaborations. Behavioral measurements and physiological recordings, including facilities for computer-based
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handling large data streams. Build foundational knowledge in AI and machine-learning approaches for predictive modeling in grain-quality assessment. Learn how deterioration in stored grain develops