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and software tools for visualizing and analyzing materials characterization data Develop novel, data-driven materials characterization workflows Advance understanding of process-structure-property
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synthetic data generation, simulation platforms (e.g., Unreal Engine, Omniverse), or virtual/augmented reality Knowledge of scientific domains such as engineering systems, physics-based modeling, or quantum
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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implementation on hybrid quantum-classical hardware, and collaboration with a multidisciplinary team of researchers in quantum computing and computational condensed matter physics. The ideal candidate will have a
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iterative solvers. Successful applications will work in applications related to gas dynamics, plasma physics, and radiation transport. The position comes with a travel allowance and access to advanced
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. Research will involve growth of single crystals and measurements to understand their structural and physical properties including magnetism and thermal transport, as well as helping to identify new magnetic
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Resources, Engineering, Physics, Environmental Science, or related discipline completed within the last 5 years. Preferred Qualifications: Strong candidates will have depth in at least one and breadth across
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physical characterization techniques (differential scanning calorimetry, dynamic light scattering, small angle neutron and/or x-ray scattering) to characterize the DIBs; and (3) Develop/implement image
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sponsorship: Visa sponsorship is not available for this position. Relocation: Moving can be overwhelming and expensive. UT-Battelle offers a generous relocation package to ease the transition process. Domestic