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research team, you will play an active role in the search for neutrinoless double-beta decay of Ge-76 with the LEGEND-200 experiment, currently operating at LNGS, help lead the R&D, design, and
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deep learning, including distributed training and/or large-scale inference using modern AI frameworks such as PyTorch. Demonstrated experience with high-performance computing systems, including multi
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experimental investigations related to understanding process effects on chemical and physical observables of nuclear fuel cycle materials; improving existing and developing novel nuclear forensics indicators
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, Mechanical Engineering or a related field completed within the last 5 years Experience in ceramics and brittle materials, experimental contact mechanics, and microstructural characterization This position
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. Demonstrated programming ability and knowledge of Python and/or C++. Experience with deep learning frameworks like PyTorch and application on high-performance computing (HPC) environments using distributed data
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-changing needs Preferred Qualifications: Experience in radiological risk assessment Experience in biokinetic model development Experience with Monte Carlo radiation transport software and applications
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Engineering, Materials Science, Materials Engineering, or a related discipline completed in the last five years. Strong hands-on experience in experimental research, including materials synthesis, device
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the last five years. A minimum of 3 years of experience in (a) design and operation of experimental thermal/fluid systems, (b) mechanical systems and instrumentation, (c) fluid dynamics, combustion, and heat
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HPC research within past five years. Preferred Qualifications: The ability to work independently and develop and deploy methods at scale. Experience in high-performance computing and software
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manufacturing technologies and manufacturing systems that could achieve transformative gains in process productivity and enhance manufacturing sustainability Strong analytical capabilities with an experience in