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FAST – Future Aluminium Structures. The positions are 3-year doctoral research fellowships starting in 2026. The PhD candidates will be embedded in the forming research activities of SFI FAST and will
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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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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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, energy production and transmission, construction of data centres and various other forms of infrastructure. More projects of this kind will be needed if aiming at continuing to follow the prevailing socio
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: elana.wilson.rowe@nmbu.no For questions regarding the PhD program (eligibility for admission, structure of the program etc), please contact: Program coordinator Josie Teurlings, email: josie.teurlings@nmbu.no via
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technical designs for pumped storage plants. The main focus will be on the tailrace tunnel system and outlet structure (pumping intake), but the research includes the whole, overarching plant design
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the timeframe ability to work independently and in a team, be innovative and creative ability to work structured and handle a heavy workload having a good command of both oral and written English via Unsplash
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standard-grounded assurance principles into a coherent theoretical structure. The PhD candidate will conduct fundamental research on how monitoring information can be used to evaluate adaptive learning
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with tunnelling Be prepared for changes to your work duties after employment. Required selection criteria You must have a relevant master’s degree in construction engineering, engineering geology
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in English Solid knowledge in finite element analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks