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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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will be part of a dynamic team working collaboratively with researchers in Q-NEXT (both at Argonne and other academic and industrial member institutions), and is expected to build on and create new
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developing LLM-based applications using Python APIs. Experience with large scale molecular dynamics (MD) packages e.g. lammps Experience with version control (e.g., Git) and collaborative software development
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optimize epitaxial growth of complex oxide nanostructures, especially ferroelectrics, via solid-phase epitaxy (SPE) Perform thin-film and device characterization across structural (XRD, AFM, SEM, XPS, TEM
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Extraction), jointly led by the Chemical Sciences and Engineering (CSE) and Applied Materials (AMD) Divisions at Argonne National Laboratory. This project focuses on understanding the evolution of structure