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24 Feb 2026 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Structural Engineering Research Field Engineering Researcher Profile
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16th March 2026 Languages English English English The Department of Structural Engineering has two vacancies for SFI FAST: PhD positions in Modelling Strength and Failure in Recycled Aluminium
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Curriculum: HEPARD’s training is carefully structured to provide core competencies essential for rigorous, policy-relevant research while offering flexibility to tailor your training to specific needs and
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arising from the inclusion of scrap, and to control the processing parameters such that the structural integrity of the cast components can meet with the demands to performance. The starting date is August
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: Digital twin development for heritage risk preparedness AI-based predictive monitoring (humidity, flooding, structural stress) Integration of GIS/BIM, LiDAR and IoT telemetry Federated data architecture and
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Technology . The position is for a period of 3 years. Desired start date: 1 May 2026 or earlier. The fellowship is part of TIES project “Tunable ion separations with micro-structured composite membranes
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such interdependencies explicit while remaining compatible with established workflows. The aim is to propose a structured representation of vessel designs that (1) integrates with current design practices and tools, (2
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Fotograf Morten Hjertø 26th April 2026 Languages English English English The Department of Ocean Operations and Civil Engineering has a vacancy for a PhD Candidate in Information Structures
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
aluminium of high recycled content. The use of post-consumer scrap (PCS) in structural components (e.g. for automotive applications) is expected to increase with growing sustainability demands. Understanding
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, and reinforcement learning for adaptive decision-making. A key aim is to connect wireless phenomena to learning robustness by combining physical-layer signal structure and signal-processing insights