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or connectivity analysis; machine-learning or deep-learning methods for geospatial analyses; ecological or remote-sensing fieldwork, particularly in alpine environments; Google Earth Engine, geodatabases or cloud
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or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via cryospheric models. A main focus of this work is snow and glaciers in the mountains around the
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/ESA_selects_Harmony_as_tenth_Earth_Explorer_mission ) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via
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/ESA_selects_Harmony_as_tenth_Earth_Explorer_mission ) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via
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) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via cryospheric models. A
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with remote sensing data (satellite, aerial, hyperspectral, SAR, LiDAR) Computer Vision Natural Language Processing Remote Sensing Machine Learening and Deep Learning Reinforcement Learning Large