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carbon capture simulation through advanced modelling tools—CapSim”. This project aims to improve CO2 capture simulation technology by apply state‐of‐the‐art techniques for evaluation of uncertainties in
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platforms. Experience in high-performance computing or working with large-scale simulation environments. Prior work involving model calibration, optimization under uncertainty, or scenario analysis
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, or equivalent research background. Your background might include experience in areas such as: Large datasets management, preferably with modeling, statistical or LCA background Uncertainty analysis, statistical
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on developing machine-learning-based or statistical emulators to approximate key outputs of complex Earth System Models, with the aim of enabling efficient uncertainty quantification, sensitivity analysis, and
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inaccuracy, irregular sampling grids, variations in measurement conditions, and other measurement uncertainties. The successful candidates should have excellent grades, strong mathematical and simulation
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simulate wind and solar forecast uncertainties on pan-European level, leveraging latest machine learning weather forecast models Apply machine learning methods to forecast day-ahead and balancing market
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operation are pivotal in laboratory environments; thus, guarantees for correct behavior despite uncertainties must be provided. This project will integrate visual sensing and force sensing to obtain effective