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Postdoctoral Appointee - Machine Learning for Multiphase Flow Modeling
In this role you will develop machine learning framework to emulate multiphase flowfields using High-Performance Computing (HPC) tools. You will also: Perform Computational Fluid Dynamics (CFD) simulations of canonical and application-specific multiphase flows. Import framework on leadership class supercomputing resources, and identify and improve the bottlenecks in scaling the emulation framework. Use emulator to accelerate two-phase flow predictions. The successful candidate’s research will involve synergistic collaborations with a multidisciplinary team involving multiphase flow modelers and computational scientists to enhance the predictive capability of multi-scale and multi-physics codes.


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