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surrogate model for giant impacts trained on SPH simulations, and then to analyse the results in the context of the geophysics, geochemistry and dynamics of the terrestrial planets. For more information and
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laser experimentation to study and control complex nonlinear dynamics. Key Responsibilities: Develop and implement Physics-Informed Neural Network (PINN) models to simulate, predict, and analyze complex
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address key challenges within these processes, constructing robust models and simulations that deepen the understanding of the underlying physics involved. The ultimate goal is to create predictive, physics
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statistical modelling, to contribute to an ARC-funded Discovery Project investigating the psychology of how people manage competing demands inside and outside of work. The project develops and tests a novel
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Multiphysics, Abaqus), CAD software (Solidworks, AutoCAD), Visual C++, LabVIEW, Pythons, EEG, and eye-tracking systems Demonstrated expertise in the area of design, methods, modelling, and optimization
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models and simulations that deepen the understanding of the underlying physics involved. The ultimate goal is to create predictive, physics-based models that optimise and control pharmaceutical
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be embedded within Moderna's Clinical and Quantitative Pharmacology (CQP) function and will contribute to key modeling and simulation deliverables for drug candidates across early and late stages
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and will contribute to key modeling and simulation deliverables for drug candidates across early and late stages of clinical development, spanning multiple therapeutic modalities. The Fellows will
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-informed model geometries and structures (e.g., using XCT datasets available within the team) and develop appropriate multiscale representations. Implement and run multiphysics simulations (e.g
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of next-generation antimicrobial materials. The role will involve performing molecular dynamics simulations and computational modelling to investigate interactions between biomolecules, materials, and