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. Experience with uncertainty quantification, Bayesian inference, inverse modelling, parameter estimation, or model calibration. Experience with high-performance computing, surrogate modelling, reduced-order
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inference pipelines for soft biomechanical systems, including differentiable physics engines, to support interpretable analysis, parameter estimation, sensitivity studies and uncertainty quantification. By
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architectures (e.g. neural ODEs, flow matching and continuous normalising flows), robustness, interpretability and uncertainty quantification; collaborate with researchers across the ACT to identify emerging
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a postdoctoral researcher, you will: Integrate hybrid traffic models and data assimilation methods into a coherent prediction framework. Develop uncertainty quantification methods and explainable and
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. Develop uncertainty quantification methods and explainable and trustworthy AI approaches. Design visualisation to support decision-making by traffic operators and strategic advisors. Collaborate closely
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, analyse, and validate innovative numerical algorithms and mathematical frameworks for problems arising in materials, fundamental physics, dynamics, optimisation, control, uncertainty quantification, inverse