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Postdoctoral Fellow in Welding Process–Performance Relationships for Recycled Aluminium Alloys

NTNU - Norwegian University of Science and Technology · Trondheim, Norway

💰 ca. 750 000–1 000 000 kr/år (estimat · vanlig lønn, ikke bekreftet)Kontraktvia NAVFrist 9.11.2026

Søk på stillingenÅpne i Havn (lagre, sammenlign, KI-hjelp)

This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You can find more information about working at NTNU and the application process here.       Video: https://www.youtube.com/watch?v=_KHQjc4ndas&t=41sAbout the position The Department of Mechanical and Industrial Engineering (MTP) at the Norwegian University of Science and Technology (NTNU) has a vacancy for a postdoctoral candidate within process–performance correlation for welding of recycled Aluminium, funded through the Centre for Research-based Innovation SFI FAST Future Aluminium Structures.  The position is a 3-year Postdoctoral research fellowship starting in 2027. The postdoctoral candidate will be embedded in the welding and structural integrity research activities of SFI FAST and will work closely with NTNU, SINTEF, and leading international industrial partners in the aluminium value chain. Your immediate leader will be the Head of Department. About SFI FAST - Future Aluminium Structures SFI FAST (Future Aluminium Structures) is a national Centre for Research-based Innovation (SFI) funded by the Research Council of Norway and the aluminum industry. The centre is led by NTNU in close collaboration with SINTEF and a broad consortium of 16 leading industrial partners across the aluminium value chain. FAST runs from 2026 to 2033 and represents one of Norway’s most ambitious long-term research initiatives within aluminium materials, manufacturing technologies, and circular economy. The primary objective of FAST is to establish the scientific and technological knowledge base required to enable the large-scale use of postconsumer scrap (PCS) Aluminium in high-value, structural, and safety-critical products. FAST adopts a through-scale and through-process research methodology, integrating materials science, manufacturing processes, product and structural design, and lifecycle performance. A key outcome of FAST is the 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 chains. Education and competence building are central pillars of FAST. The centre will educate more than 18 PhD and postdoctoral researchers and over 100 MSc students, strengthening Norway’s international position in Aluminium research and innovation.  Please see the centre’s website: Future Aluminium Structures (FAST) - NTNU Duties of the position This postdoctoral position aims to address the following topics as critical steps toward the reliable fabrication of recycled aluminum structures through sustainable, data-driven welding routes. Therefore, the main tasks of the position are as follows: • Process Parameter Development: Development and implementation of optimal arc and solid-state welding characteristics  • Monitoring & Control: Developing an in-situ monitoring and control system to detect, characterize, and correct/mitigate defects during welding • Characterization: Evaluating the microstructure and mechanical performance of fabricated aluminum components • Process–performance correlation and data-assisted analysis: Establishing experimentally grounded relationships between real-time process monitoring data, microstructure, and mechanical performance to support process optimization and reliability assessment. • Proposal development: Development of nationally (NFR) and internationally (EU) funded proposals, particularly with close collaboration with industry. • Collaboration of PhD and M.Sc. projects: You will have a central role in connecting WP4 and WP5 in SFI FAST, where two PhD students will work on Welding Process Development and Fatigue Performance and Enhancement • Dissemination: Presentation of research outcomes in the SFI FAST Center and publishing the project outcomes in high-quality journals  • Contribute to project meetings, workshops, seminars etc. You will report to Prof. Hosseini and Assoc. Professor Nima Razavi. Required selection criteria • You must have completed a doctoral degree in Mechanical Engineering, Materials Science and Engineering, Production Technology, Welding Engineering or related topics.  • Documented track record in welding process research, including fusion and solid-state welding • If you are in the final phase of your doctorate and can document that the thesis has been submitted or that the defense date has been confirmed, your application can be considered even if you have not defended. NOTE: Documentation of the obtained doctorate must be presented before you can take up the position. • Documented track record in material testing with particular focus on testing fatigue and fracture behavior of welded or AM structures  • Documented track record in welding metallurgy and materials characterization • Experience with hands-on experimental welding, robot programming, and testing work  • Excellent English language skills, oral and written • Good track record of publications in Q1 journals If you do not already have educational competence that meets the requirements for a position as associate professor in Norway, NTNU will arrange for you to acquire such competence during the employment period. In such cases, you will also be assigned relevant teaching as part of the career-promoting work. The appointment is to be made in accordance with NTNUs guidelines for recruitment positions and for general criteria for the position. Preferred selection criteria • Documented track record in processing of Aluminium alloys • Basic knowledge of robotic welding • Knowledge in the development of weld monitoring systems • Background in modelling (like thermal, thermodynamic, machine learning)  • Industrial experience, particularly in the Nordic region •

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