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structural models and compute electronic and vibrational properties. Develop and train neural-network or other machine-learned interatomic potentials to enable large-scale molecular dynamics (MD) simulations
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: Proficiency in machine learning, statistical modeling, and quantitative methods for multi-omics data analysis Molecular Simulations: Expertise with molecular simulation tools like OpenMM, AMBER, Gromacs, and
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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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staff scientists, engineers, postdocs, and students, with an active research portfolio that spans: Leading electron-scattering experiments at Jefferson Lab (CLAS12, SoLID, Hall A, B, and C) Major
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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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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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(HPC). The postdoc will work closely with visualization researchers, AI scientists, and domain application teams across Argonne and the broader DOE ecosystem. The goal of this postdoctoral position is to
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undergraduates. Postdocs benefit from strong interactions with experts in applied mathematics, computer science, device physics, materials science, and statistics, as well as access to world-leading supercomputing
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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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). This position will focus on ultrafast dynamics in femto- to nanosecond time-domains in quantum materials including nonequilibrium phase transitions and collective excitations in quantum materials, including