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specializing in artificial intelligence for science, knowledge-guided machine learning, and scalable scientific AI workflows. The successful candidate will conduct research at the intersection of machine
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discovery. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially
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, machine learning, and statistical modeling methods for multimodal manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and
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Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
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opportunity to work with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The
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expertise in multiscale and multifidelity simulation techniques ( ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or
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seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in
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lasers, optics, and optical emission spectroscopy. Experience designing custom experimental hardware, vacuum systems, and plasma diagnostics. Experience applying machine learning methods for
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another, work together, and measure success. Basic Qualifications: A PhD in materials science & engineering, physics, chemistry, or a related field completed within the last 5 years
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, optomechanics, optical cavities, and/or photonic quantum sensing and computing. Experience with control electronics, data acquisition systems, machine learning and AI for control and optimization of