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internationally recognised for its expertise in computational fluid dynamics (CFD), multiphase and multiphysics flow, and nonlinear solid mechanics, supported by access to world-class high-performance computing
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coupled nuclear engineering problems, using techniques such as (but not limited to) molecular dynamics, computational fluid dynamics, activation decay codes, kinetic Monte Carlo codes, particle transport
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disciplines including aerospace, combustion, design, fluid mechanics, materials, mechanical, mechatronic and robotics engineering. To learn more about the School click here . The Clean Combustion Group
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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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the triggers and processes involved in releasing and transporting Cu-metal-rich fluids from the deep crustal magmatic plumbing system to facilitate new Cu and critical metal ore discoveries required
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of Mechanical, Medical and Process Engineering, Faculty of Engineering. The Artificial Heart Frontiers Program (AHFP) is seeking a highly motivated Mechanical Engineer with a specialisation in Computational Fluid
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I offer projects broadly related to supernova explosions and the final stages in the lives of massive stars. Specific topics of interest include fluid dynamics processes in stellar explosions and