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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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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