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modeling, AI-driven simulations, optical remote sensing and biogeochemical modeling to predict seagrass distribution under various climate and nutrient scenarios. SEAGUARD aims to provide science-based
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, multi- layered spatial data sets (e.g. Remote Sensing and GIS), and contributing to the growth of the interdisciplinary Integrated Coastal Studies Program, which focuses on issues related
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of the University of Kiel, and the remote sensing company EOMAP GmbH & Co. KG. Who are we? The IOW is an independent research institute of the Leibniz Association for which equal opportunities, family friendliness
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or equivalent Skills/Qualifications Advanced skills in using GIS and remote sensing solutions; Advanced skills in statistical data processing; Experience in organizing events for disseminating scientific results
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. The fellow will be part of an active team with expertise in Geophysical Prospection, Geographical Information Systems (GIS), Satellite Remote Sensing, Computational methods, Database design, and more
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research skills, particularly in statistical modeling, geospatial analysis, and health metrics evaluation. Experience working with a variety of spatial datasets, including remote sensing data, for health and
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with GIS, remote sensing, and geospatial data mining ;- Excellent communication and collaboration skills ;- Excellent written and oral communication skills in English. Proficiency in French or German
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The successful candidate will have a recent PhD in environmental science (geography, ecology, biology, earth sciences, engineering); the candidate has a sound knowledge of spatial analysis methods and tools (GIS
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experience in geo-spatial analysis and working with remotely-sensed satellite imagery Demonstrable experience of image analysis (for example, using Google Earth Engine or PlanetScope) Experience of programming
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confidence in predictions from multisource geophysical data. - Multiscale Data Fusion: Integrating geophysical data across various resolutions and sources, including remote sensing, into unified predictive