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, durability testing under harsh environmental conditions, fabrication and full-scale structural testing of sleepers under static and cyclic loading, numerical modelling, lifecycle and sustainability
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NTNU Norwegian University of Science and Technology | Trondheim, Sor-Trondelag | Norway | 16 days ago
oriented. By deploying electrochemical techniques under non-reversible thermodynamic considerations and alongside numerical and machine learning analysis methods, this work will advance a high-level
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NTNU Norwegian University of Science and Technology | Trondheim, Sor-Trondelag | Norway | 24 days ago
engineering, computational mechanics or a related field, with strong analytical and computational skills and an interest in probabilistic methods. The position is full-time for three years. Preferred
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tasks: Numerical modelling of shared mooring configurations; Wave-basin testing of FPV farms; Analysis of FPV farms co-located with floating breakwaters; and Assessment of farm-level energy production and
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. Responsibilities The researcher will contribute to the work package focused on advancing offshore solar farms. The work package involves five project partners and includes the following tasks: Numerical
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professions (clinical decision-making, professional responsibility, effects on trust, patient–provider relations, etc.). We welcome empirical, analytical, normative, and mixed-methods approaches. The
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research directions within the CISU context. The candidates' parallel projects enable direct comparison and knowledge transfer across urban contexts. Both candidates will be part of SFI-CISU, which during
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aims to develop novel methods and statistical software that are tailor-made to the research questions and data of CREATE. The successful candidate will conduct advanced methodological and
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research covers a broad range of topics, including strategy, digitalization, innovation and entrepreneurship, marketing, and communication. Researchers in the group uses a variety of research methods
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core areas are also encouraged to apply. Relevant research areas include: AI & Physics: AI methods for physical discovery, machine learning for field theories and complex simulations, physics-informed