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the case of ForestSphere, the goal is to reconstruct the terrain, vegetation cover, buildings, and infrastructure, as well as the meteorological environment that can influence wildfires, using sensor data
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: Application (cover letter) Vision for teaching and research for the tenure track period CV including employment history, list of publications, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio
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about the lab and their work can be found by visiting https://sarvestanilab.com/ About the Postdoctoral Scientist role: We are seeking a motivated and creative Postdoctoral Scientist to join our dynamic
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. For instance, in Computational Hydraulics and Environment, the selected candidate will work on enhancing the reliability of data collected from various sources such as river flow sensors, rainfall gauges, and
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scientific highlights, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process
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time missions at CINaM in Marseille Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7643-CATHEN-002/Candidater.aspx Requirements Research FieldChemistryEducation LevelPhD or equivalent
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for sensor development activities. Work on aerosol oxidative potential assays, their applications and underlying mechanisms. Guide students. Analyze data and present the results both orally and in written form
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the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises: An stipend for 3.5 years (currently £20,780 p.a. for 2026/27) in line with UK Research
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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to study soil-vegetation-atmosphere interactions. Your main responsibilities will include: Fly drones and process data of multispectral, thermal and LiDAR sensors Implement approaches to integrate drone data