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The overarching focus of the postdoc position and effort is on advancing the understanding of materials at high pressure-temperature conditions, and in particularly across the solid-melt transition
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platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, physics-aware workflows, and strong computational chemistry/electronic-structure expertise. The researcher will
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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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-the-loop exploration of extreme-scale scientific data. This position sits at the intersection of scientific visualization, agentic AI systems, human–computer interaction (HCI), and high-performance computing
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staff members, two engineers, and postdocs and students. Our program spans electron-scattering experiments at Jefferson Lab in Hall A, B, and C, including CLAS12 and SoLID. We have led SeaQuest and are
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computational research. They are intrinsically driven, goal-oriented, and can work collaboratively with others. Working closely with the CPS divison, the postdoc will leverage AMReX and the LBM to develop
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quantum transduction and terahertz (THz) photon generation via enhanced light–matter interactions. The postdoc will lead efforts in device patterning and the integration of complex materials—such as
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and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction. The appointees will work within a highly collaborative team spanning multiple DOE user
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four staff members [Ian Cloët, Alessandro Lovato, Anna McCoy, and Yong Zhao] and several postdocs and students. The group has a broad research program in QCD/hadron physics and nuclear structure
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal