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Plan and execute in situ/operando experiments using advanced characterization methods, including Near Ambient Pressure X-ray Photoelectron Spectroscopy (NAP-XPS), electron microscopy, Raman spectroscopy
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, co-simulation, or hardware-relevant environments. Primary Responsibilities: Develop machine learning and AI methods for control, optimization, and cyber-resilient operation of distribution systems, DER
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engine modeling code. Perform high-fidelity CFD simulations of turbulent and reacting flows pertaining to gas turbines and detonation engines using spectral element method (SEM). Perform scalability
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The Applied Materials Division at Argonne National Laboratory has an immediate opening for a Postdoctoral Appointee. The candidate will be responsible for reviewing and developing design methods and
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. Position Requirements A formal education in Physics, Materials Science, Chemistry, or a related field at the PhD level with zero to five years of employment experience. Demonstrated experience with high
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-ion battery materials. The focus is to develop atomic layer processing methods to tailor the surface properties of lithium ion battery electrodes using nanoscale thin films and surface treatments
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regular channels. Position Requirements A formal education in chemical engineering, mechanical engineering, or a related field at the PhD level with zero to five years of experience. To perform
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and integrate emerging AI techniques (e.g., agentic workflows, LLMs) into scientific problems, ensuring methods effectively solve real domain challenges. Advanced Model Development: Design and debug
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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devices, with emphasis on lithium-ion, sodium-ion, and lead-acid battery systems. Modeling and analysis will leverage tools such as COMSOL, MATLAB, Excel, Python, and related scientific software. The role