21 ocean-atmoshphere-modeling PhD positions at NTNU Norwegian University of Science and Technology
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, DeepFDM, MINO, etc., but also other methods for generative models in function spaces. Develop multiscale (resolution-invariant) AI models for wave kinematics and sea loads on ships, considering also phase
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application process here. About the position The Department of Materials Science and Engineering (IMA) at the Natural Science Faculty, has a vacancy for a position as PhD candidate related to modelling
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marine technology, together with more than 60 PhD students from all over the world. You will explore how emerging AI technologies—foundation models, generative design tools, agent platforms, reasoning
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estimation, and learning-based prediction models that anticipate the future motion of vessels seen in the radar data, based on the radar data, local geography and historical patterns. The methods
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regulations that provides both incentives and constraints for the maritime energy transition and emission reduction. The research objective of the PhD is to develop models that capture the interaction between
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clearance to the seabed or sea surface can strongly affect performance, structural integrity, and fatigue life. These effects vary with water depth and turbine concept. This PhD project will investigate how
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cell walls, which have been implied in responses to the two parasites. We will also use the model species thale cress (Arabidopsis thaliana) as a resource to help identify the molecular mechanisms and
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knowledge for a better world and solutions that can change everyday life. Department of Marine Technology We develop methods and technology related to the blue economy: oil and gas extraction at sea, ship
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designing, developing and evaluating systems and models to enhance learning through AI technology. The PhD fellow will engage with developing and evaluating models and agents, as well as, multi-agent networks
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the broader framework of Embodied AI. The goal is to integrate physical models with deep learning to create interpretable, data-driven observers that enable physically grounded perception and control for robust