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19 Jan 2026 Job Information Organisation/Company CNRS Department Centre d'études spatiales de la biosphère Research Field Environmental science Biological sciences Geosciences Researcher Profile Recognised Researcher (R2) Application Deadline 9 Feb 2026 - 23:59 (UTC) Country France Type of...
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atmospheric sciences • Knowledge of cloud or aerosol physics • Experience in algorithm development and satellite remote sensing • Good written and spoken English • Ability to work independently as well as in a
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combining space-based remote sensing and modeling, it aims to better understand the evolution of forest fuels and their role in fire propagation. Tested on pilot forest areas (Centre-Val de Loire and Pyrénées
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functional molecular or nano-scale systems that can be remotely controlled, notably by light. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7053-PHIPIE-002/Candidater.aspx Requirements
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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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. Atmospheric measurements (in situ and remote sensing) provide a robust method to assess and improve emission inventories and to monitor the effectiveness of reduction measures. The project will develop
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in the use and exploitation of high spatial and temporal resolution remote sensing data. An interest in causal discovery and inference is more than welcome. The candidate will be required to interact
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climate and geospatial databases. Experience in GIS/Geomatics and remote sensing data analysis. • Languages: French and English (spoken and written) are essential, given the regional context and
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misalignments. 4. Dissemination and contribution to the mission • Present results to CNES, ESA, and industrial partners (Airbus, Winlight Optics). • Contribute to conferences in atmospheric remote sensing and
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characterize the spatio-temporal contexts that favor crises. • Development of advanced predictive models (multivariate approaches, machine learning) combining event data, snow and weather data, and remote