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synchrotron X-ray scattering data, including X-ray diffraction and total scattering measurements. The research will emphasize extracting physically meaningful descriptors from complex scattering datasets and
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at conferences and ALCF/DOE venues. Position Requirements Required Skills and Qualifications: Ph.D. in Computer Science, Physics, Chemistry, Biology, Engineering, Mathematics, or a related computational discipline
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facilities in partnership with the computational science community. We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and
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-driven initiatives within computational science Effective communication skills, both verbal and written, for effective collaboration with interdisciplinary teams and clear presentation of complex technical
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complexes Small gas-phase molecules and organic chiral compounds Compute accurate transition moments beyond the electric-dipole approximation and simulate x-ray observables, including x-ray absorption, x-ray
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datasets, complex simulations, and multimodal information. This position provides the opportunity to work with some of the world’s most advanced computing resources, including flagship exascale
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is supported by a DOE-funded research program on ultrafast science involving Argonne National Laboratory, University of Washington, and MIT. The goal of this research program is to understand and
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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collaboration with team members. Skilled written and verbal communicator, including the ability to present complex information so that it is understandable to a broad audience. Computer skills relevant for data
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simulations and experiments across scientific user facilities, leveraging data to understand complex material phenomena across scales. Key Responsibilities Design, implement, and validate physics-informed AI/ML