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centered around a unique, open-source digital platform enriched with data and powered by domain knowledge-based advanced machine learning and artificial intelligence capabilities. By introducing a Digital
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intelligence, machine learning, statistical estimation methods, software tools, and big-data frameworks. Programming languages such as e.g. Python, C++, and LABVIEW. Emission control rules and regulations in
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computer vision models for forest-based 3D point cloud data. In recent years, large advances have been made for deep learning algorithms for high-resolution point clouds from small geographic areas. We seek
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detailed data about forest ecosystems. To convert the captured data into meaningful information about the forest environment we seek a PhD candidate who wishes to advance state-of-the-art computer vision
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have an academically relevant background within Learning Technologies, Interaction Design, Human-Computer Interaction (HCI), Human-Centred Computing or alike. You must have a Master's degree in Computer
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relevant background within Learning Technologies, Interaction Design, Human-Computer Interaction (HCI), Human-Centred Computing or alike. You must have a Master's degree in Computer Science or equivalent
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fellow, PhD Candidate, research assistant, and specialist candidate. PLEASE NOTE: For detailed information about what the application must contain, see paragraph “About the application”. The appointment is
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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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comprehensive databases combining nationwide Norwegian health and socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning