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prediction reliability and robustness Estimate path flows, boundary conditions, and other key inputs for large-scale traffic models Design scalable methods for real-time traffic prediction and uncertainty
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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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, analyse, and validate innovative numerical algorithms and mathematical frameworks for problems arising in materials, fundamental physics, dynamics, optimisation, control, uncertainty quantification, inverse
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trustworthiness: developing and validating trustworthy AI methods for space applications, with emphasis on explainability, robustness, uncertainty quantification, safety assurance and compliance with relevant