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imaging and spectroscopy modalities Ultrafast and in situ/operando techniques Advanced detector technologies and correlative approaches that reveal structure–function relationships Contribute to and enhance
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involvement in three SciDAC-5 projects: 1) Femtoscale Imaging of Nuclei using Exascale Platforms, 2) Fundamental nuclear physics at exascale and beyond, and 3) Nuclear Computational Low Energy Initiative
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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with
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the laboratory. Preferred Knowledge, Skills, and Experience Familiarity with accelerator modeling and simulation codes. Demonstrated experience operating an accelerator independently and conducting beam
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”, “Firstname_Lastname_cover_letter”. Include links to code examples in your CV (e.g., GitHub page, past project repositories). Position Requirements A recent PhD (completed within 5 years, or soon to be completed) in computer science
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, scattering, imaging) including uncertainty quantification, interpretability, and reproducibility Autonomous and semi-autonomous experimentation for materials synthesis and characterization (closed-loop
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workflows, and immersive or experimental interfaces Integrate LLM-based and agentic AI systems with scientific visualization frameworks, in situ pipelines, and data analysis workflows Prototype and evaluate
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., specific code you wrote, modules you debugged, or workflows you designed). Highlight Transferable Skills: If your background is in a specific science domain (e.g., Physics, Biology), frame your experience in
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regulations and contract. Skill in modeling, processing, and analyzing computational results to inform accompanying experimental efforts. Skill in the use of modern collaborative coding practices Demonstrated
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis