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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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, and contributing to a robust data infrastructure that makes large-scale, multimodal datasets FAIR and AI-ready. You will publish findings in high-impact journals, present at major international
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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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, interdisciplinary environment with access to large-scale computing resources and diverse scientific use cases. The position strongly supports publishing in top-tier venues, contributing to open-source research
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running simulations or AI workflows on supercomputers Experience with training or applying large language models for research Experience with MPI and Input/Output (I/O), and data management Experience in
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emphasize multi-wavelength survey science, the galaxy-halo connection, cluster cosmology, and large-scale cosmological simulations. Analysis efforts cover topics such as CMB power spectra, CMB lensing, galaxy
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design, develop, and evaluate AI-driven scientific visualization assistants that support intuitive, context-aware interaction with large-scale simulation and experimental data. The postdoc will focus
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and application of nonlinear x-ray spectroscopies. The successful candidate will work closely with experimental collaborators conducting measurements at large-scale x-ray user facilities, including
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(NUCLEI); and two Topical Collaborations: 1) 3D quark-gluon structure of hadrons: mass, spin, and tomography, and 2) Nuclear Theory for New Physics. Further information on our group and its research
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to contribute to other large-team scientific projects in materials engineering, chemistry, and beyond at Argonne National Laboratory. Position Requirements Required skills: Recently completed PhD (within the last