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Student or Scientific Assistant for Remote Sensing Data Processing and Cloud-based Workflows (f/m/d)
or Scientific Assistant for Remote Sensing Data Processing and Cloud-based Workflows (f/m/d) Your tasks: Support in processing satellite and UAV remote sensing data on cloud platforms Adaptation and optimization
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: Plant health. Agricultural data management: plant and climate. Remote sensing. Professional Experience: In field trials. Have provided technical support in agricultural research. Experience in functions
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, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve
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large, multi-institution, project to advance the remote sensing of freshwater resources. The research will be conducted at the Environmental Remote Sensing Laboratory, under the direction of Dr. Hongxing
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Scientist / Scientific assistant – Tree species detection using deep-learning Are you an ambitious data scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and
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Development & Alumni Affairs (DHDAA) supports a flexible work environment by offering on-site (in office 5 days a week), hybrid (in office a minimum of 2 days a week or defined by manager), remote (work off
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: Starting Pay - $79,300; Commensurate with education and experience Work Hours: Monday - Friday 8:00am - 5:00pm, occasional nights and weekends Remote Eligible: This position is not eligible for remote work
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: https://policy.ucop.edu/doc/1001004/Anti-Discrimination Because we want you to feel seen and valued, our recruitment process at UC Davis supports openness and authenticity. Research shows that some
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analysis of in situ measurement data and spatial remote sensing data - Participation in the scientific supervision of students - Participation in national and international mobility programs The project is
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, underwater gliders, micro-AUVs, and ship-based sampling), remote sensing products, and high-resolution numerical models. The work will improve understanding of coastal ecosystem variability, support better