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Professional qualifications (required) Relevant PhD degree (e.g. computer science, machine learning, statistics) Experience in developing deep learning models for 3D point cloud data Strong programming skills
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techniques based on 3D sensor perception and deep learning to estimate forest tree structure from high-resolution drone-based LiDAR point clouds and imagery contribute to a world-leading large-scale forest
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, such as sedimentation, meltwater flow, and vegetation change, into active drivers of adaptive design. This interdisciplinary work combines advanced computational tools, including 4D point cloud modeling and
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PhD degree is required. Develop protocols for real-time phenotyping of NUE-related traits using the TraitFinder phenotyping system. Build and validate digital twin models using 3D point cloud and
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and Signal Analysis (CMVS) at the Faculty of Information Technology and Electrical Engineering, University of Oulu. CMVS is a creative, open, and internationally attractive research unit, combining the
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tools, including 4D point cloud modeling and state-of-the-art machine learning and deep learning techniques (such as generative adversarial networks), with empirical fieldwork in Norwegian glacier
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, such as sedimentation, meltwater flow, and vegetation change, into active drivers of adaptive design. This interdisciplinary work combines advanced computational tools, including 4D point cloud modeling and
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and point-cloud streams are massive, wireless links are unreliable, and safety demands that information be both timely and trustworthy. IONIAN tackles this bottleneck head-on. We will re-invent multi