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, R., & Herrmann, H. J. (2016). Particle-Fluid-Structure Interaction for Debris Flow Impact on Flexible Barriers. Computer-Aided Civil and Infrastructure Engineering, 31(5), 323–333. https://doi.org
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Computational Fluid Dynamics (CFD) solvers into Python-based AI frameworks (e.g., PyTorch, JAX) to enable high-efficiency, scalable simulations of aerosol transport systems. The developed model will incorporate
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weather forecasting to cardiovascular medicine. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning
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) and Chemical C Computational (CC) teams at UM6P. The work aims to develop robust computational models to capture the Multiphysics behavior— including fluid flow, heat transfer, and reaction dynamics
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. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning - constitute fundamental scientific domains that act as
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known for its work in fluid dynamics, one of its recent activities being quantum computation of fluid dynamics (QCFD). Expectations Candidates will be responsible for the QCFD work. The successful
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funding. References 1. Small-scale reconstruction in three-dimensional Kolmogorov flows using four-dimensional variational data assimilation (https://www.cambridge.org/core/journals/journal-of-fluid
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Initiates and leads research and development in the areas of aerospace-related fluid dynamics of canonical as well as complex propulsion systems using scale-resolved simulations, reduced-order modeling and
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will be responsible for developing and applying thermal and dynamic models for modelling behaviour of a liquid hydrogen tank, coupled to the behaviour of the contained fluid, as well as aspects of tank
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concepts from fluid dynamics and statistical physics, the candidate will analyze the resulting turbulent-like flows through their energy spectra and correlation functions. The candidate will also analyze