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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 3 months ago
and analyze data from our INSTEP network in conjunction with others. Sensor data fusion along with remote sensing instruments (Pandora, AERONET) and satellites (TROPOMI, TEMPO) will serve to validate
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networks), and the integration of these ground-based observations with state-of-the-art remote sensing information describing forest structure and function. The position will require computationally
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decision-relevant outputs such as restoration and implementation scenarios. The postdoc will collaborate closely with experts in remote sensing, ecology, environmental science, and engineering while
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decision-relevant outputs such as restoration and implementation scenarios. The postdoc will collaborate closely with experts in remote sensing, ecology, environmental science, and engineering while
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students, and 100+ undergraduate majors. Research and graduate education in the Department of Earth Sciences focuses on the following subfields: (a) Geophysics: tectonics, seismology, remote sensing, and
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evaluate advanced networked systems that enable new forms of remote care and emergency support services under realistic conditions. You will be mainly in charge of: • Contributing to the design and
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective
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close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective
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to climate, environment, or sustainability challenges. • Required skills: o Strong quantitative background, with expertise in one or more of the following: statistical modeling, machine learning, remote