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will receive full consideration. Key Responsibilities AI-ready data and analysis for the ePIC Barrel Imaging Calorimeter and our Jefferson Lab program Support for the PRad-II and X17 experiments
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perform advanced synchrotron experiments to probe structural, chemical, and dynamic evolution of defects in thin films and heterostructures. Utilize techniques such as Bragg coherent diffraction imaging
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related field. Experience with finite element simulations and developing constitutive models. Knowledge of high temperature creep crack growth. Knowledge of engineering design codes such as the ASME Boiler
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with DFT or electronic-structure codes such as VASP, Quantum ESPRESSO, CP2K, ABINIT, GPAW, Gaussian, ORCA, Q-Chem, or related packages. Strong materials science or chemistry domain knowledge, such as
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. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC
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thin film deposition is preferred. Advanced image processing and analysis skills. Experience with micromagnetic simulation is preferred. Ability to work independently as well as in collaboration with a
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or image processing Experience with AI-assisted or feedback-driven fabrication workflows Interest in quantum photonic platforms, electro-optic systems, or light–matter coupling physics Application Materials
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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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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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have a strong background in fundamental electrochemistry, with preferable hands-on expertise in computational materials science. The applicant should be well versed in code development, application of AI