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studies (e.g., EELS, EDS) to probe defect structures and dynamics Apply advanced image processing and analysis; develop AI/ML workflows for quantitative defect characterization Implement high-throughput and
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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applying machine learning or other elements of artificial intelligence to solving significant scientific or engineering problems Interest in software development, with particular emphasis on the Python
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. The ANL ATLAS group maintains strong involvement across the experiment, including detector operations, TDAQ upgrades, Software and Computing, ML development, and High-Performance Computing (HPC
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contribute to research and model development to enhance the resilience of domestic and global supply chains for clean energy technologies. Lead technical and policy analysis to inform decision-makers
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laboratory partners, and contribute to the development of separation technologies for energy, water, and critical resources. Key Responsibilities: Develop and apply in-situ methods (e.g., optical coherence
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Separations Postdoctoral Research Associate will develop capacitive deionization systems for the selective recovery of critical materials and also investigate electrode aging, degradation, and durability
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insights and develop reduced order models (ROMs) for boundary layer flows and turbulent combustion. Integrate ROMs with CFD solvers and demonstrate predictive accuracy compared to traditional modeling
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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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. The candidate is expected to work as part of a collaborative team of chemists, engineers, and materials scientists to develop new processes for converting CO2 and CH4 to valuable products. The candidate should