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. The primary focus of this role will be in developing laboratory methods to improve recovery of microbial genomes from complex samples. Secondary focus of this role will be in maintaining cultures
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integration Optimization and stochastic modeling methodologies Energy storage Electricity market analysis Supports multidisciplinary teams in the application of these methods and tools to complex issues in
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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The Multiphysics Computations Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing high-fidelity scale-resolving computational fluid dynamics (CFD
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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density
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artificial intelligence/machine learning (AI/ML). The successful candidate will contribute to the group’s broad physics program, which includes precision Higgs and Standard Model measurements, and searches
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at technical conferences. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, computer
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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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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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well as university partners on coastal methods and validation strategies. Mentor summer students on data analysis and visualization workflows. Publish in peer-reviewed journals, present at scientific conferences, and