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Field
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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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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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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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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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acquisition, instrumentation and monitoring during laboratory testing Perform numerical modelling and simulation, such as finite element modelling, parametric studies, and calibration against test data. Analyse
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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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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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-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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implementation of a coupled Computational Fluid Dynamics (CFD) and fire dynamics model within the OpenFOAM framework. This model will simulate battery thermal runaway and subsequent fire propagation. The
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structural and functional data, will present new routes to improved control of spores. You will use modelling and simulation tools to perform molecular dynamics simulations of SleB and of some of its partner