27 model-checking PhD positions at NTNU Norwegian University of Science and Technology
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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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candidates within Modelling Strength and Failure in Recycled AluminiumAlloys funded through the Centre for Research-based Innovation SFI FAST – Future Aluminium Structures. The positions are linked
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. Digital twin technologies offer a promising approach for modelling and monitoring such complex systems. However, the ongoing energy transition also introduces significant uncertainty due to fluctuating
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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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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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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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. The candidate will study architectural models, including the placement and roles of quantum repeaters, memories, and control-plane functions, and how these integrate with classical networking and orchestration
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hydrodynamic load models. The purpose of this work is to increase the understanding of longlines with seaweed under sea loads and develop accountable hydrodynamic load models. Your supervisor will be Professor
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, Mechanical Engineering, Manufacturing Engineering, Industrial Engineering, or related field of the actual PhD position. Your course of study must correspond to a five-year Norwegian course, where 120 credits
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related algebraic and analytic structures for the analysis and modelling of complex sequential data. Path signatures, originating in stochastic integration and rough path theory, provide expressive