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. The applicant must have an academically relevant education corresponding to a five-year master’s degree or a cand.med.vet. degree, with a learning outcome corresponding to the descriptions in the Norwegian
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. You will become part of a dynamic, collaborative working environment with expertise in drilling engineering, geomechanics, machine learning, and energy systems. The project will integrate real‑time
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learning, and energy systems. The project will integrate real‑time drilling data, digital‑twin methodologies, and AI‑driven optimization approaches to address fundamental challenges in hard‑rock drilling. As
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and secondments. • Blended Learning Approach: Our training combines intensive in-person workshops at partner institutions with regular interactive online seminars, journal clubs, and research
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
, using advance equipment like SEM, TEM, P-FIB, etc. A strong academic record, with excellent analytical and problem-solving skills. Experience in using relevant modelling methods for material or process
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, employing multivariate analysis (MVA) and advanced data-driven approaches, including artificial intelligence (AI) methods. The material mapping results will be validated using complementary analytical
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artificial intelligence (AI) methods. The material mapping results will be validated using complementary analytical techniques applied to the same artworks. In addition, style characterization of the paintings
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-year master’s degree, with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second cycle. The applicant must have a documented strong academic background
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må du være oppmerksom på at enkelte funksjoner og tjenester ikke kan brukes, da de forutsetter at nettsiden kan huske de valgene du gjør. Du kan velge bort cookies fra Google Analytics her . Det er