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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 project will combine field measurements and laboratory analysis, using cutting-edge techniques including dynamic contact angle measurement, X-ray diffraction, mid-infrared spectroscopy, and synchrotron
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forms part of a collaborative ARC Discovery Project between the University of Adelaide and Southern Cross University. The overarching goal is to improve understanding of aluminium dynamics in acid sulfate
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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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fracture outcomes using a static locked femoral nail or a femoral nail in the dynamic mode with a sliding lag screw. Clinical factors (including analgesic requirements, Time up and go) and radiological
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AEMO’s annual General Power System Risk review AEMO must model the power system at a 5 year ahead time horizon. Modelling at this time horizon while accounting accurately for power system dynamics in PSSE
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understanding of gene presence/absence, structural variations, and evolutionary dynamics. In this project we will aim to develop novel dynamic programming computational methods for pangenome assembly of diploid