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Department of Forest Resource Management The Department of Forest Resource Management conducts education and research in the areas of forest planning, forest remote sensing, forest inventory and
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remote sensing, forest inventory and sampling, forest mathematical statistics and landscape studies. The department is also responsible for the implementation of the ongoing environmental monitoring
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7 Dec 2025 Job Information Organisation/Company Faculty of Science, Charles University Research Field Computer science » Other Geography » Cartography Technology » Remote sensing Researcher Profile
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skills in remote sensing, AI, ecological modelling, and policy engagement, working across disciplines and continents. The project includes an industrial supervisor to support non-academic training and
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3 Fully Funded PhD Students positions!📍 Remote Sensing Laboratory, McMaster University, Hamilton, Ontario, Canada🗓 Start Date: Spring or Fall 2026The Remote Sensing Laboratory at McMaster
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12.11.2025, Wissenschaftliches Personal Join our team in a collaboration between TUM and Politecnico di Milano! Develop cutting-edge methods combining remote sensing, physics-based modeling, and
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to support the management of forest resources and ecosystems. The PhD candidate will focus on integration of the extensive amount of data collected by forest machines with remote sensing data to further
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Instituto de Ciencias del Patrimonio - Spanish Council for Scientific Research (CSIC) | Spain | about 1 month ago
self-generated data in the field-, and to explore the potential of remote sensing to unlock the relationship between vegetation history and the distribution of ancient economic and settlement remains
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this centre, we are opening a PhD position on remote sensing of mountain forest dynamics. The Technical University of Munich (TUM) is one of the leading universities globally. At the TUM School of Life Sciences
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organization. We invite applications from motivated candidates with passion and a strong interest in conducting research and innovation activities on crop modeling, remote sensing, and computer vision to predict