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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
for model refinement. Perform multi-scale simulations (e.g. DFT / atomistic / phase-field simulations) to train AI/ML models. Conduct scientific research on ferroelectrics and/or 2D memristive materials
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analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation. Collaborate with a
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. The goal of this work is to investigate the dynamics of beams with intense space charge and benchmark simulation models against experimental results. As a U.S. Department of Energy (DOE) Office of Science
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simulations for fermionic and Hubbard-like materials models • Collaborate within a multi-disciplinary research environment consisting of quantum computing experts, computational scientists, and condensed
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of scientific AI. Focus Areas: Cross-Domain Interoperability: Develop common readiness templates, standardized metadata models, and APIs to enable seamless integration across diverse scientific domains
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models that can describe rates and locales of precipitation. Major Duties/Responsibilities: Conduct independent research using atomic-scale simulation with rare event methods to understand hydroxylation
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) for lattice model simulations. Experience working in a multi-disciplinary research environment. Demonstrated written and oral communication skills, a proven publication record, and effective interpersonal
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Proficiency in the use of industry standard modeling and simulation tools, such as spreadsheet-based process cost modeling, input/output modeling, and commercially available life cycle analysis tools such as
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and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self-motivated
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applications, (2) design and architecture of integrated, hybrid, atomistic simulation software packages (e.g., LAMMPS) and DL models, and (3) documentation, verification and validation, and software quality