36 structures "https:" "https:" "https:" "https:" "https:" "https:" "University of Southampton" positions at Argonne
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fabricate nanoscale electrical test structures (e.g., photolithography, e-beam lithography) Design, test, and characterize radiofrequency (RF) circuitry and measurement approaches Analyze and interpret data
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synchrotrons and x-ray free-electron lasers. Key Responsibilities Perform electronic-structure calculations using ab initio quantum chemistry methods and software (commonly CASSCF-based approaches) Investigate a
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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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critical thinking skills; intellectual curiosity. Able to structure and formulate solutions to complex problems. Highly motivated and detail oriented with the ability to work independently and in close
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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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/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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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
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optimize epitaxial growth of complex oxide nanostructures, especially ferroelectrics, via solid-phase epitaxy (SPE) Perform thin-film and device characterization across structural (XRD, AFM, SEM, XPS, TEM
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Extraction), jointly led by the Chemical Sciences and Engineering (CSE) and Applied Materials (AMD) Divisions at Argonne National Laboratory. This project focuses on understanding the evolution of structure
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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