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(CFD) models, including fluid–structure interaction (FSI) simulations where appropriate, will be developed and validated against experimental measurements. These models will be used to investigate
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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comprehensive validation framework will be established, beginning with offline simulations using detailed distribution network models to test and benchmark the proposed control strategies. These developments will
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to computational models. The group has a leading role in European within the fields of unconventional computing, our Comet initiative , and Computer Architecture, our CAL initiative . The position will be part of
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-economic study, design and analysis of integrated systems. Experience with energy system modeling/simulation tools (e.g., MATLAB/Simulink, Modelica, and COMSOL Multiphysics), energy systems analysis
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and decision support for railway operations and maintenance. The successful candidate will work on developing, testing, modelling, and validating methods for DAS-based monitoring of railway
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of future reactor systems with a focus on systems relevant for Norway. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations
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to high-performance computing facilities and datasets from laboratory experiments will be provided to support simulation and verification of the resulting model. Replicate and learn a theoretical model for
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intervention studies to determine the most effective ways for healthcare professionals to collaborate in the best interest of the patient, and we delve into health economics to establish models for cost savings
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. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations of reactor transients and quantify how surrogate uncertainties propagate