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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 8 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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of this project for a one-year period (100% full-time commitment) to make a significant contribution to the implementation of machine learning (ML) algorithms. The postdoc is expected to have proven experience in
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 8 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) | Fields Landing, California | United States | about 8 hours ago
improve estimation of rates of snow accumulation, snowmelt, ice melt, and sublimation from snow and ice worldwide at scales driven by topographic variability. We seek projects focusing on the use of machine
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At the Technical Faculty of IT and Design of the Department of Sustainability and Planning, Copenhagen, a position as Postdoctoral researcher in Geospatial Machine Learning for Predicting Land Use
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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other
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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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motivated post-doctoral associate with a strong background in control systems and machine learning to join the research team of Prof. M. Umar B. Niazi. The position focuses on the development of digital twins
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: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 8 hours ago
that are facile with computationally efficient, rigorous machine learning for image region identification, demonstrate an understanding of both planetary and scalable computer science, and have publication