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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 7 hours ago
. Description: Participant will develop integrated photonics technology for space-based remote sensing applications. Integrated photonics is used for light creation, manipulation, processing and detection in a
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The Department of Environmental Science at Aarhus University (AU-ENVS), Roskilde, invites applications for a 2-year position as postdoc in satellite remote sensing and land-use modeling of grassland
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that integrates multi-source observational data (e.g., eddy covariance flux towers, satellite remote sensing, and atmospheric datasets) to investigate how SAI could influence ecosystem carbon fluxes and
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observed by smart cameras and satellites in relation to environmental variables such as liquid flow, watercourse slope, river types, etc Where to apply Website https://pica.cineca.it/unipd/ Requirements
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applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future. Job Summary The Residential College Drama Program https
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-annual Where to apply Website https://www.unimore.it/it Requirements Additional Information Eligibility criteria Eligible destination country/ies for fellows: Italy Eligibility of fellows: country/ies
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Systems, Thematic Cartography, and Remote Sensing: software such as QGIS, QFIELD, or ArcGIS (0 to 5 points) ▫ Training in cartographic sources and CAD (0 to 5 points) ▫ Training in Spatial Data
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Board (SUAAB) programming board. SUAAB is a student programming board that offers a variety of engaging and innovative events that promote student involvement and a sense of belonging within the UTD
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evaluation of advanced applications for network benchmarking, road hazard warning, and pseudo-remote driving supported by computer vision, while also supporting the broader research and integration activities
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in surface water bodies using multispectral remote sensing imagery, through the development of frugal artificial intelligence models capable of efficiently exploiting heterogeneous data while