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computational scientists, economists, engineers, and other researchers to develop data-driven, decision-relevant analytical tools for complex industrial systems. Key Responsibilities: Develop, improve, and apply
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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-informed AI framework that decodes the complex relationships between material defects, functional fields (e.g., strain, electrostatic potential), and device performance, with a primary focus on leveraging
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(BCDI), Laue microdiffraction, ptychographic laminography, and X-ray photon correlation spectroscopy (XPCS) to study strain, dislocation networks, voids, and interfacial morphology. Develop in-situ and
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facilities in partnership with the computational science community. We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and