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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
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of analytics into production systems Knowledge of experimental design, uncertainty quantification, scientific machine learning, or digital twin methodologies Experience collaborating across national laboratories
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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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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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
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to experimental data analysis. Make advances in one or more of: multimodal data fusion/joint inference; uncertainty quantification with realistic noise/measurement error; complex physics- and artifact-aware forward
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methods with optimization and decision-support models. Background in one or more of the following: time-series analysis, neural networks, forecasting, uncertainty quantification, sensitivity analysis
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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