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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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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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. This position is part of the DOE-BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC), a multi-facility collaboration integrating experimental measurements, simulations, and data science to
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facilities. Perform thermochemical and thermophysical properties measurements of MSR relevant molten salts to understand the impact of evolving salt composition on property values and provide this data
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microwave resonator design, characterization, and RF/microwave measurement techniques Electromagnetic simulation experience (e.g., Sonnet, Ansys HFSS/Lumerical, or similar tools) Experience with data
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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/modelers, and data scientists Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of Materials Science, Chemical Engineering, Chemistry, or a closely related field
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The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge computational and systems biology research. The primary focus
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be