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of Photogrammetry and Remote Sensing and together with other chairs being part of the RTG. Requirements: good or very good university degree in electrical engineering, computer science, computer engineering or
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: You will conduct research in the field of experimental atmospheric physics, with a focus on optical remote sensing of the atmosphere up to the edge of space (mesosphere and lower thermosphere, MLT
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worldwide. Research methodology The researcher will combine existing landslide inventories with new remote sensing observations to build multi-year databases of landslide occurrences and control factors (i.e
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of Photogrammetry and Remote Sensing and together with other chairs being part of the RTG. Requirements: good or very good university degree in computer science, mechatronics, robotics, electrical engineering, or a
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
or Geoinformatics, or related areas. Candidates will be assessed according to the project team’s needs regarding geospatial data and remote sensing for analysis, interpretation, and application in environmental
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remote sensing, numerical modelling, and fieldwork. We recognize that no candidate will have experience in all of these areas, and we encourage you to apply if you have the following qualifications
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to observations. Finally, you will work on improving the drought response of the vegetation in PCR-GLOBWB. Throughout the process model improvements will be compared to local and remotely sensed observations during
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improvements will be compared to local and remotely sensed observations during extensive validation and sensitivity analysis. A personalised training programme will be set up, reflecting your training needs and
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 12 days ago
. Managing Director Mike Frieser: „We believe that diversity is the key to groundbreaking research and innovative solutions. Our flexible working models and remote options create a supportive environment for balancing
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to develop and apply advanced remote-sensing approaches and AI-assisted image analysis to investigate the distribution, diversity, and spatial dynamics of Antarctic lichen communities, thereby contributing