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) at Norwegian University 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
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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 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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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 2 months ago
tools for solving these problems. The goal of this PhD is to understand how these results could be integrated or inspire the designing process of the models for the deployment and the execution of Cloud
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) Positions PhD Positions Country Spain Application Deadline 17 Nov 2025 - 13:59 (Europe/Madrid) Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Jan 2026 Is the job
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PhD studentship: Automated Landslide Detection for Emergency Response Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional
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modeling in calculation processes and application of AI techniques for processing dense point clouds Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research
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reinforcement learning for edge-cloud-based computation. In both cases, vehicles and robots must be able to navigate around obstacles and manage safety constraints along the planned trajectory, even in situations
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passion for technology and the difference it can make in meeting the UN’s Sustainable Development Goals, perhaps you are one of our three new PhD students. At the Maersk Mc-Kinney Moller Institute (MMMI
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data