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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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research in: recyclable & durable materials fast, data-driven surrogate models lifecycle-conscious design to reduce costs, increase reliability, and accelerate the North Sea’s ambitious 300 GW target by 2050
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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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, data analytics, or decision-support systems Manufacturing systems modelling, simulation, or optimisation Production planning and control or operations management Industrial digitalisation and data-driven
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
development of the FAST Virtual Lab, a digital framework combining experimental data, physics-based models, and data-driven methods to support design, manufacturing, and decision-making across aluminium value
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engineering and knowledge-driven systems at the department. Participate in international activities such as conferences and research stays at foreign educational institutions, i.e. the relevant partners. Be
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Engineering at NTNU, where computational mechanics, advanced finite element modelling, and artificial intelligence meet. As a PhD candidate, you will work at the forefront of nonlinear simulation, contributing
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13 Mar 2026 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Ocean Operations and Civil Engineering Research Field Computer science
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will be used as a laboratory through both fieldwork campaigns and numerical model development. The candidate will identify hotspots of eddy generation driven by instabilities and their control on cross