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material design process. Some potential key research objectives: AI Model Development: Create machine learning models to predict FGM properties based on compositional gradients and processing conditions
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implement a framework to infer anisotropic viscosity from both ice and mantle textures in a numerical flow model. This will open new avenues for understanding solid earth and cryosphere dynamics, and their
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) and economics (or related fields). Applicants must have experience in one or more of the topics: Model-predictive control Numerical optimization Econometrics Virtual power plants Power systems and/or
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consisting of both control theory (or related fields) and economics (or related fields). Applicants must have experience in one or more of the topics: Model-predictive control Numerical optimization
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, numerical modelling, experiments and theory act in concert. The center includes the Oslo-branch of PoreLab, which is a Center of Excellence (CoE), the former CoE, Physics of Geological Processes (PGP) and
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. They typically contain a combination of numerical, categorical, and textual data, and are often multilingual. DPPs are still evolving, facing challenges in data processing, handling missing or redundant data, and
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between ice and mantle dynamics. In DYNAMICE, we will implement a framework to infer anisotropic viscosity from both ice and mantle textures in a numerical flow model. This will open new avenues