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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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support the optimization of hydrogen storage systems. · PhD in fluid mechanics, energy engineering, chemical/process engineering, or related field · Strong background in Computational Fluid Dynamics (CFD
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data driven approaches (experimental dataset physics based model dataset) to achieve process optimization, defect prediction and property prediction in various AM processes. The additional specific
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research in radiochemical separations and advanced aqueous recycling of used nuclear fuel (UNF). The successful candidate will contribute to the development and optimization of advanced aqueous recycling
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generation, conversion, and utilization in different electro- and thermo-mechanical systems where the study of heat transfer, fluid mechanics, reactive flows, as well as solid mechanics plays a central role
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contribute to the following activities: - Design and optimization of functional formulations containing luminescent nanoparticles - Study of colloidal stability, rheological behavior, and dispersion quality
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the research team; contributing to the optimization of the investigated enhancement geometries; disseminating research outcomes through publications in leading international journals and presentations
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, qualification, and deployment of AI agents and models, Computational Fluid Dynamics (CFD) simulation codes, and Finite Element Method (FEM) based tools for nuclear energy (fission and fusion) applications
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the simulation of turbulent flows using a tensor network representation of the Navier–Stokes equations. Unlike recent approaches based on tensor networks, which simulate fluid flows in physical space using finite
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-disciplinary environment? Information Many of the most challenging problems in science and engineering involve physical systems that evolve over time while exhibiting rich mathematical structure. Fluid flows