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Field
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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communication and collaborative skills. Experience with SLAM, sensor fusion, LiDAR/depth camera data processing. Familiarity with deep learning for obstacle avoidance (e.g., map-less navigation). Background in
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 2 hours ago
from multiple methods and developing cross-sensor capabilities (e.g., combining SAR and hyperspectral) will be essential for deriving meaningful insights for biodiversity and ecological conservation
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nanomaterials to novel cellulose-based textiles. Key responsibilities Conduct a comprehensive sustainability background analysis of different material options for optically adaptive textiles including sensors and
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, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research groups around the world. The employment
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nanophotonics, lasers, quantum photonics, optical sensors, LEDs, photovoltaics, ultra-high speed optical transmission systems, bio-photonics, acoustics, power electronics, robotics, and autonomous systems
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research section comprised of over 50 members dedicated to advancing micro- and nanotechnology-based sensors, detection systems, and drug-delivery devices. If you are a highly motivated individual with
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following topics: in situ sensor installation and flood monitoring, GIS & geospatial big data, AI/ML and data science approaches for hydrologic predictions, risk analysis. Experience in working with big data
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 3 months ago
the combination of socioeconomic data and geospatial information. Experience with the calibration and deployment of low-cost sensors is preferred. Occasional travel may be necessary. Field of Science: Earth