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superconducting behavior, utilizing NV centers in diamond and spin defects in 2D materials in variable-magnetic-field cryogenic environments. The candidate will leverage single- and ensemble-spin based sensors
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and optimize molten salt thermophysical property measurements, develop and utilize theoretical models and frameworks to predict salt properties, molten salt thermophysical property database expansion
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, molecular dynamics simulations using ab initio and machine-learning potentials, and the development or application of machine-learning tools for feature extraction, property prediction, and inverse molecular
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to the performance of high-power accelerators such as the Spallation Neutron Source (SNS). The goal of this work is to demonstrate predictive modeling of beam distributions in a realistic accelerator, and as such
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to the performance of high-power accelerators such as the Spallation Neutron Source (SNS). The goal of this work is to demonstrate predictive modeling of beam distributions in a realistic accelerator, and as such
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strategies, including model predictive control and reinforcement learning. Preferred Qualifications: Proactive self-starter with the ability to work independently and contribute creatively in collaborative