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mixed models, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree
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, semantics, functional relationships, and actionable affordances, and enabling predictive reasoning to bridge gaps when observations are missing or unreliable. As an optional extension, a learned world-model
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research and academic bodies. This collaboration is centered around a unique, open-source digital platform enriched with data and powered by domain knowledge-based advanced machine learning and artificial
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AI systems are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s
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qualities required for such purposes. Digital terrain models will be used to efficiently map cultural remains under forest canopies. The second project (Mapping Natural Forests in Norway) focuses on mapping
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to ensure that the admission requirements are met, must be uploaded as an attachment. Main tasks Develop machine learning models to produce forest information at local and landscape scales Develop machine
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involve applications of the existing framework and advancement in the interface towards integrated assessment and energy system models for scenario analysis. The selected candidate will join a team of
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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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of Life Sciences (NMBU) has a vacant 3-year PhD–position related to developing deep learning models for 3D forest point clouds. The position is part of "SmartForest" (www.smartforest.no ), a center for
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learning, e.g. using JAX, based on numerical models such as Higher Order Spectral method, mcsimpy, etc. Collect real metocean data from relevant online databases, datastreams such as from R/V Gunnerus, and