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9th November 2025 Languages English Norsk Bokmål English English PhD Scholarship - Deep learning for forest point clouds Apply for this job See advertisement Key Information The position is part of
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of physics into machine learning and deep learning architectures to create accurate, physically consistent, efficient and interpretable/generalizable models. This PhD project will contribute to the development
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://mountainsinmotion.w.uib.no/ ), but there is also flexibility for the candidate to incorporate additional field data. This PhD project offers a great opportunity to work with large-scale biodiversity and climate datasets
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inference methods, survey design, and/or machine learning Experience with web scraping and API-based data collection Organizational and coordination skills, such as assisting in drafting terms of reference
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of imaging data such as structural MRI and functional MRI, preferably ultra-high field imaging is required Experience in machine learning methods and analyzing big datasets is desirable Experience in
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comprehensive databases combining nationwide Norwegian health and socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning
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machine learning, e.g. predicting rate of penetration (ROP) and wear. Investigate the possibilities in automation and robotization and the use of artificial intelligence. Electric drilling and other methods
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that combine principled reasoning with the efficiency of modern machine learning to enable intelligent, real-time decision-making in large-scale interconnected systems. This position offers the opportunity
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in crystalline rocks. Drilling optimization using machine learning, e.g. predicting rate of penetration (ROP) and wear. Investigate the possibilities in automation and robotization and the use
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epidemiology, causal inference, genetic epidemiology, and machine learning. As a PhD candidate in the project, you will: Actively participate in group meetings, design statistical analysis plans in collaboration