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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, large-scale computational science, and simulation of networked physical systems Familiarity with techniques for sensitivity analysis and handling high-dimensional problems Experience in power grid
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Applications are invited for post-doctoral positions in the Cosmological Physics and Advanced Computing Group (CPAC) Group in Argonne National Laboratory’s High Energy Physics (HEP) Division
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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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This position focuses on the research and development of novel radiation detectors and associated edge-computing circuits and algorithms for X-ray, particle, and nuclear physics experiments
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Because of the drastically increasing demand from AI/ML applications, the computing hardware industry has gravitated towards data formats narrower than the IEEE double format that most computational
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Biology: Strong background in systems biology and regulatory network modeling Interdisciplinary Collaboration: Experience working across disciplines with computational biologists, computer scientists, and
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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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The Q-NEXT National Quantum Information Science and Research Center based at Argonne National Laboratory invites applications for a postdoctoral position to conduct research in the field of material
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, or network-based modeling of infrastructure or industrial systems. Familiarity with high-performance computing, cloud computing, or parallel computing environments for training models and solving optimization
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-Informed Neural Networks (PINNs) and geometric deep learning. Experience with active learning, agentic workflows, or other methods for autonomous experimentation. Familiarity with high-performance computing