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UiO/Anders Lien 25th June 2026 Languages English English English PhD Research Fellow in Professional/Workplace Learning with Emerging Epistemic Technologies Apply for this job See advertisement
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representation through machine learning. The position is for a fixed term of 3 years and is part of the project “Reaching AI Projections Trustworthy for Unseen Rainfall Extremes (RAPTURE)”, funded by a European
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on addressing this question which lies at the heart of understanding high-impact flooding in an ever warmer and wetter world. RAPTURE brings together high resolution physical simulations, machine-learning climate
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new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials
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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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, Julia, C/C++ or similar is required. Experience with machine learning, analysis of climate or high-resolution model output, climate predictions/projections or environmental risk assessment is an advantage
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, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno-economic study, design and analysis of integrated systems. Experience with energy system
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many areas of applied and theoretical statistics and data science, and is heavily involved in research at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to
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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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at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to conduct cutting-edge research on AI-based forecasting and analytics for shipbroking and maritime decision support