24 structures-"https:"-"https:"-"https:"-"UCL"-"UCL" Postdoctoral positions at Argonne
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candidate will participate in RNA project for structural characterization of RNA by SAXS, x-ray crystallography, and Cryo-EM. The main role for the project is to produce RNA samples in large scale by using in
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and simulation of the electronic structure of polycrystalline and defective two-dimensional materials. Interviews will begin immediately and continue until the position is filled. This position centers
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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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/ML model development to design and discover redox-active materials with tunable properties (structure, charge state, etc.) Discovery of novel materials for energy storage and conversion and their
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phonons, magnons, and other emergent quasiparticles. The successful candidate will lead time-resolved X-ray diffraction experiments to reveal structural and electronic dynamics in condensed matter systems
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and unravel structure-function relationships. This position is suited for a highly energetic and self-driven researcher willing to work in highly collaborative teams. This position will involve a
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental
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: - Comprehensive understanding of applied computational materials science, including electronic structure methods and molecular dynamics. - Experience with High-Performance Computing (HPC) systems and intelligent
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Argonne National Laboratory’s Accelerator Science Division is seeking a Postdoctoral Appointee to contribute to the development of a Sub- THz Collinear Structural Wakefield Accelerator
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models for composites of arbitrary structures to predict their homogenized properties. The candidate will also work closely with AI experts to develop workflows for composite structure discovery given