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involves development of deep learning based synthetic data generators that obtain both good utility and protection of privacy, through tailored model approximation, as well as new measures of privacy and
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background in machine learning and statistics, who are eager to contribute to cutting-edge methods for generating private and fair synthetic data with good utility. This project involves development of deep
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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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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 globe. Candidates with
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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
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), it must be equiva-lent to a master in the Norwegian educational system Documented proficiency in scientific programming (e.g., Python) Documented proficiency in deep learning frameworks (e.g., PyTorch
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UiO/Anders Lien 3rd July 2026 Languages English English English PhD Research Fellow in Active Learning for Arctic observing systems Apply for this job See advertisement About the position Position
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try again. UiO/Anders Lien 3rd July 2026 Languages English English English PhD Research Fellow in Active Learning for Arctic observing systems Apply for this job See advertisement About the position