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the scientific discovery process by quickly extracting useful information from vast quantiles of data, and by providing feedback to the experiment system enabling autonomous control and steering. New fast data
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experts across system software, power management infrastructure, performance characterization, networking, and novel computer architectures and accelerators. It will also involve collaboration with leading
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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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The Chemical and Fuel Cycle Technologies division is seeking a Postdoctoral Appointee to join a multidisciplinary team developing electrochemical reactions and processes in molten salt electrolytes
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national security. With guidance, the appointee will: Develop and implement processes for chemical separations for nuclear-energy relevant and other industrial chemical applications Develop tools and
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to innovative measurement techniques and novel technologies, like superconducting nanowire detectors and pixelized 3D-printed MCP-PMTs, capitalizing on Argonne's multidisciplinary expertise. Application Process
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during the application process. Any further questions regarding this position should be addressed to Dr. Peter Mueller (pmueller@anl.gov ). Review of applicants is ongoing and will continue until
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the cleanroom with standard nanofabrication process flow Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range
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programs, e.g., through Laboratory Directed Research and Development (LDRD) funding, or through external Funding Opportunity Announcements (FOAs) from the DOE Office of Nuclear Physics. Application Process
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such as PyTorch and TensorFlow. Experience with high-performance computing and/or scientific workflow. Strong background in inverse problems, numerical optimization and image processing. Job Family