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- NTNU Norwegian University of Science and Technology
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- NTNU - Norwegian University of Science and Technology
- UiT The Arctic University of Norway
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Field
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wind propulsion devices. The work will focus on data-driven modelling, routing optimization, and control strategies for wind-assisted vessels. Relevant research topics include voyage optimization under
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of Materials, analytical and numerical Data-Driven Engineering Design and Optimization Algorithms Surrogate Modeling (e.g., Kriging, Gaussian Processes, Neural Networks, etc.) Scientific Programming (e.g
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tomography, with numerical simulations informed by microstructural data. The successful candidate will work at the interface between experiments, modelling, and data-driven methods. Particular emphasis will be
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innovative approaches in bit technology, hydraulic hammer systems, drilling fluids, and thermal management. The project will combine experimental insights, physical modeling, digital‑twin technologies, and AI
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. You will explore how emerging AI technologies—foundation models, generative design tools, agent platforms, reasoning engines, and reinforcement learning—can be adapted and extended for maritime design
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approaches for identifying, modelling, and integrating uncertainty factors originating from IoE devices and system dynamics, combining data-driven learning with knowledge-based modelling techniques
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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uncertainty factors originating from IoE devices and system dynamics, combining data-driven learning with knowledge-based modelling techniques. The developed methods will be evaluated in representative energy
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches