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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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that can flow without resistance, mimicking the behaviour of quantum fluids. These systems, known as quantum fluids of light, promise revolutionary applications in low-energy photonic devices, including
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the role of stress-controlled structural permeability in hydrogen migration. The project will also investigate the petrophysical and fluid-flow properties of fault rocks and interrogate paleo-episodes
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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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will involve a novel comprehensive approach, including analyses of cellular fate such as reduction/oxidation in diverse biological fluids, tumour cells and tissues, the transport into and within cells
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of the complex non-Newtonian fluids present in this system. This Project addresses a significant scientific knowledge gap in the understanding of the rheology and thermal behaviour bulk plastic melt-flows
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-time, internal student (unless approval for part-time and/or external study is obtained) have a background in mechanical or biomedical engineering, expertise in fluid mechanics, benchtop experimental
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technological solutions for the recycling of waste heat in specific food industry settings, using computational fluid dynamics modelling, lab experiments and field work To disseminate finding in high impact
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? RMIT Engineering PhD candidates get a laptop, world-class labs, research funding, travel support, and seminars. Join RMIT’s PhD project in non-Newtonian fluid dynamics with a $35,888/year stipend for 3.5
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in control systems and fluid-structure interaction. The students will be supervised by Prof Khac Duc Do and the co-CIs of the project. Student type Future Students Faculties and centres Faculty