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contribute to the Lab’s broader effort in conversion and separation of carbon-based materials. The role will require the individual to work with personnel that perform machine learning and molecular
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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. Working within an interdisciplinary team, you will develop frameworks that connect atomistic features, mesoscale dynamics, and device-level performance. The effort will integrate heterogeneous data from
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) of electrochemical energy storage devices (diagnosis) and predict the SOH into the future (prognosis). The primary projects this postdoc will contribute to relate to lithium-ion batteries, advanced lead-acid batteries
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to the Lab’s broader effort in CH4 and CO2 utilization R&D. The role will require the individual to work with personnel that perform machine learning and molecular simulations and electrochemical device testing
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all areas of experimental condensed matter physics will be considered, particular emphasis will be placed on the dynamical studies of 2D materials and their functionalities. This postdoctoral position
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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vitro transcription with T7 RNA polymerase, subsequent purification necessary for SAXS, MX and Cryo-EM. The work requires experience in molecular biology for basic cloning and protein expression
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phenomena Create new reduced-order models and submodels related to fluid flow, heat transfer, thermochemistry, and electrochemistry in reactive systems Use modeling tools such as computational fluid dynamics
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would be part of a dynamic team working collaboratively with researchers in Q-NEXT (both at Argonne and other member institutions) on both hardware development and network-scale benchmarking of memory