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for quantum information science, but many open questions remain regarding how to control the morphology and crystallinity of these host materials for exemplary performace as hosts for optically addressable spin
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skills and qualifications: A recent PhD (completed within the last 5 years) in computer science, electrical engineering, or a related field. Strong background in network interconnect design and
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focus will be on exploring the utility of artificial intelligence models in the study of the above, studying quantum mechanical generalizations, and uncovering connections to statistical models. Position
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manuscript to be submitted). Position Requirements This level of knowledge is typically achieved through a formal education in electrical engineering, mechanical engineering, physics, or a related field at the
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The Multi-Physics Computations group at Argonne National Laboratory is seeking to hire a postdoctoral appointee on the topic of CFD modeling of internal combustion engines fueled by low-carbon fuels
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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electrolyzer materials and electrodes using synchrotron X-ray techniques at Argonne’s Advanced Photon Source (APS). In particular X-ray absorption spectroscopy, X-ray scattering, and nano-computed tomography
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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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is supported by a DOE-funded research program on ultrafast science involving Argonne National Laboratory, University of Washington, and MIT. The goal of this research program is to understand and
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, computational physics and x-ray science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of the world’s largest supercomputers (Polaris