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for energy and quantum technology applications. The research encompasses the development of methods and codes to study the interaction between materials and light, and the study of specific materials, in
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developing advanced AI/ML models for applications in physics, chemistry, or materials science Experience with periodic simulation codes such as VASP Proficiency in Python programming Excellent written and oral
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Venkat Srinivasan, Director of the Argonne Collaborative Center for Energy Storage Science: [email protected] More information on ESRA and the research areas of focus can be found at: https
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using computing clusters for simulation and data analysis. Experience with accelerator modeling and simulation codes, such as Elegant and Cheetah. Experience with SDDS, Tcl/Tk, and scripting in Linux
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Publications: 1. P. Chen et al., Ultrafast photonic micro-systems to manipulate hard X-rays at 300 picoseconds, Nat Commun, 10:1158 (2019). https://doi.org/10.1038/s41467-019-09077-1. 2. P. Chen et al., Optics
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learn from experiments at unprecedented scale. For more information: Rapid Prototyping Lab: https://rpl.cels.anl.gov/ Autonomous Discovery at Argonne: https://www.anl.gov/autonomous-discovery MADSci
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candidate will develop a novel multiscale multimodal experimental apparatus with precise control during data acquisition, as well as work on data processing pipeline, adaptable to imaging of energy-conversion
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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