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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
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. The research group is seeking a talented Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear stability theory, modeling & identification, optimal control, certifiably safe
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compartmental models for RSV developed within the STAMP-RSV program by tailoring an established software library for individual simulation to the Australian RSV transmission context. Information to parameterise
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satellites, with the potential for travel to test instrumentation in ideal locations. Additionally, the simulation work will focus on developing computational models to validate instrumentation and optimising
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are linked to research on composite hydrogen tanks, composite propellers for drones and finite element modelling of textile manufacturing. All research will be conducted with leading companies in
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the molten pool. However, these models are computationally intensive and impractical for widespread simulations of large-scale part deposition. This project aims to develop a novel FEA-based approach
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this astonishing picometre fabrication precision. Further aims of the project include: Theoretical modelling of nanoscale effects and processes in SNAP Development of experimental methods of picometre-precise