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scalability studies to identify and improve bottlenecks in large codes. Experience in development of data-driven reduced-order models in one or more of these areas: turbulence, boundary layer flows, combustion
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Microscopy (LTEM) studies of magnetic heterostructures. We are interested in using real space imaging techniques to understand the evolution of energy landscapes that lead to emergent domain behavior in
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. The successful candidate will have the opportunity to work with interdisciplinary research teams, collaborate with universities, industry, and national laboratory partners, and contribute to the development
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-generation nuclear science experiments at Jefferson Lab and the Electron-Ion Collider (EIC). As part of our growing multidisciplinary team, you will contribute to the development of superconducting nanowire
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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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of scientists and High-Performance Computing (HPC) engineers. In the AL/ML group, we work at the forefront of HPC to push scientific boundaries, carrying out research and development in state
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within the last 0-5 years) in computational science, mathematics, physics, or a related field with a focus on image processing. Proven experience in algorithm and software development. Expertise in Python