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printing), enabling new design opportunities and advanced construction techniques. The research methodology combines theoretical modelling and experimental validation. The work will begin with an extensive
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of physics into machine learning and deep learning architectures to create accurate, physically consistent, efficient and interpretable/generalizable models. This PhD project will contribute to the development
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conduct background checks on potential candidates. Please note that all our candidates may be asked questions necessary in this context. This includes questions about any connections to countries
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are offering a fully funded PhD position in the field of Physics-informed Learning-based Control. This interdisciplinary research area bridges control theory, machine learning, and physics-based modeling
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Technology » Energy technology Environmental science Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 31 Oct 2025
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models to resolve blade loads and structural responses under both operational and extreme conditions, including scenarios with partial out-of-water exposure Uncertainty quantification to ensure robust and
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of realistic inflow environments and their impact on turbine performance Assessment of clearance effects on loads and efficiency near the sea surface and seabed Development and use of multi-fidelity models
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access. The goals of such access include supporting registry operations as well as health care research. Of particular interest in this context are differentially private algorithms for statistical model
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of smart technologies to visualize yard operations in a digital form (such as virtual models and digital twins). Smart technologies can collect, analyze, and represent data from various sources
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both water tanks and with phase change material - PCM). Digital twins of the system for real-time decisions, based on petroleum field experience. LCA and economic conciderations. Modeling and reservoir