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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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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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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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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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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
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-resolved prediction of wave elevation and wave loads in continuous time. Look into fusing the models into a foundational model. Setting up a synthetic training gym as a platform for AI model learning, e.g
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world where 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