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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's doctoral program (https://www.ntnu.edu/nv/phd) PLEASE NOTE: For detailed information
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the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Candidates without a master’s degree have until 31.08.2026 to complete the final exam. The purpose of the fellowship is
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posted here will be among the first ones to start in the centre. Please see the centre’s website: Future Aluminium Structures (FAST) - NTNU PhD Position 1: Modelling Plastic Flow and Fracture in Recycled
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the Telemark Canal , focusing on digital twin-based preparedness modelling for cultural heritage infrastructure. The primary objective of the position is to complete a doctoral education leading to a PhD degree
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. The Department of Geosciences is one of eight departments in the Faculty of Engineering. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/298154/phd-candidate-in-hard-roc… Requirements
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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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Stage Researcher (R1) Positions PhD Positions Application Deadline 17 Apr 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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possibility of extension) Start date: August 2026 at latest The students will be enrolled in the structured PhD programme in Computer Science at Sapienza University of Rome, Italy: https://www.uniroma1.it/en
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advancing resilient and sustainable infrastructure? As a PhD Candidate with us, you will work toward earning your doctorate while developing strong expertise in probabilistic modelling, structural reliability
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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