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and machine learning with density functional theory, or other similarly relevant computational methods, to advance understanding of materials design predictions for 2D and 3D systems with electronic and
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 9 hours ago
of Yuhua Duan. This project will be hosted at the NETL Pittsburgh, PA campus. Although material modeling with artificial Intelligence/machine learning (AI/ML) applications and experimental instrumental
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testing. A campaign layer, driven by Bayesian optimization, decides which experiment to run next. The postdoc will own the system architecture below that layer: the PLC and instrument control, the software
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. The researcher will gain an educational experience in multi-scale computational modeling and machine learning of mechanical behavior of high temperature alloys. They will be a part of interdisciplinary team and
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machine learning within the scope of ACAG. The postdoc will have various opportunities for professional development, including contributing to and leading grant proposal development (e.g., external
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of electrochemistry and artificial intelligence. Ideal candidates will have experience in machine learning, large language models, AI-agent development and computational workflows, with particular interest in building
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, and machine learning within the scope of ACAG. The postdoc will have various opportunities for professional development, including contributing to and leading grant proposal development (e.g., external
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/or oceanography; or experience working with numerical simulations, large data, scientific programming, and/or machine learning. Applicants are asked to send a CV, a brief statement of research
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devices and equipment to support research projects that may include use of Makerspaces, machine shops, advanced manufacturing, or supervising instructional design teams; may involve use of Solidworks
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 9 hours ago
migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter estimation tools and large-scale 3D geologic model for gas