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programme aims to advance fundamental understanding of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key
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expected to contribute to other activities and tasks within the ENER-G project. The main outcome(s) of the PhD will be novel optimisation models and solution algorithms that will substantially advance
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studies of ices or materials. For PhD 3: experience or affinity with numerical modelling, fluid dynamics, or computational physics. Strong analytical skills and the ability to work independently and
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, architectural engineering, (applied) physics, applied mathematics, or a related field is required. Strong mathematical and analytical skills are essential. Solid understanding of numerical modeling and signal
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with spatiotemporal data analyses, remote sensing and numerical modelling; the ability to independently plan and organise the research, and to take a leading role in its direction; strong oral and
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project uses a novel experimental community model system of wild Drosophila species and their parasitoids from tropical Australia. We are able to perform multigenerational laboratory microcosm experiments
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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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of dark matter production through axion domain walls; • Study of scalar field interaction phenomena; • Analytical and numerical development of models for gravitational and electromagnetic signals
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/Postdocs and more senior scientists within the MicroAM project. Responsibilities and qualifications If you are you interested in numerical simulations of advanced manufacturing processes which involve
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numerical modelling and programming is an advantage; demonstrated ability to work both independently and collaboratively in an interdisciplinary and multicultural research environment; good organizational and