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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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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
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the last 5 years Preferred Qualifications: Strong background in experimental systems related to heat and mass transfer systems. Knowledge of CFD tools and analytical modelling is preferred. Experience with
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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for radiation protection and develops many of the biokinetic and dosimetric models recommended by the International Commission on Radiological Protection (ICRP) and applied by U.S. federal agencies
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materials that may serve as model systems displaying quantum behaviors. It will also provide opportunities for collaboration with quantum computing efforts within the Quantum Science Center, guiding and
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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hardware. As part of our team, you will perform research to develop new scalable quantum simulation algorithms, based on multi-linear representation theory, and apply them to real world applications spanning
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expertise with Python-based programming for instrument control, image analysis or spectroscopy Preferred Qualifications: Experience with transmission electron microscopy Familiarity with application
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modeling techniques and make fundamental contributions to the field. Interact with other researchers, technicians, and students to shape and drive the research agenda. Present and report research results and