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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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other sources to train and validate AI models. Develop computational workflows incorporating LLMs, Monte Carlo Tree Search (MCTS), phylogenetic inference, uncertainty quantification, and epidemiological
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, instrumentation, modeling, and data science Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field(s) of materials science, physics, computational science, or a related field
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scientists with extensive microelectronics (materials and devices), AI, computational materials science and materials characterization expertise; and will be expected to bring the electrochemical expertise
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agencies and other national laboratories. The candidate will develop power systems and electricity market modeling, and analytics tools that support energy, economic, and financial analyses of power grid
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral researcher position in the field of hybrid quantum computing. This exciting project
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phenomena Create new reduced-order models and submodels related to fluid flow, heat transfer, thermochemistry, and electrochemistry in multiphase systems Use modeling tools such as computational fluid
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modeling tools to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
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We invite applications for a Postdoctoral Appointee to contribute to a growing research program in process systems modeling and optimization for clean energy, critical materials, and advanced