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at ORNL, along with computational tools for integrated atomistic modeling in support of materials research for extreme environments. The candidates will develop and apply advanced experimental
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science, and materials engineering, with emphasis on understanding material behavior in complex chemical and radiological environments. Research activities may include the design of functional nanomaterials
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of advanced materials. Research efforts will include the application of density functional theory packages and in-house codes, and the development of supplemental numerical tools, to describe
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ferroelectric and ferroelastic materials, under external stimuli such as electric fields, light, strain, and temperature. This position resides in the Data Nanonanalytics (DNA) Group within the Nanomaterials
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funded by the U.S. Department of Energy (DOE) Office of Basic Energy Sciences (BES) in the Materials Sciences and Technology Division (MSTD). The successful candidate will be expected to work effectively
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research at the intersection of quantum information science and fundamental materials science focused on understanding the coherent dynamics of optically accessible spins in bulk and van der Waals materials
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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‑correction, calibration, and adaptive data‑acquisition methods to improve measurement efficiency and throughput Apply physics‑based or computational transport modeling to interpret internal material gradients
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) and Computational Fluid Dynamics (CFD), for polymer composite manufacturing processes Perform multi-physics simulations involving coupled thermal, mechanical, and material behavior across multiple
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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model