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respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in materials science and engineering, physics, chemistry, electrical engineering, or a
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modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The candidate is expected
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: Experience applying machine learning methods for predictive analysis. Expereince with the Python programming language. Experience with the creation, validation, and use of synthetic data for constructing
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, instrumentation, and data acquisition systems; conduct laboratory and field testing; and perform thermodynamic analysis, system modeling, and performance assessments. Analyze and interpret experimental and modeling
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Quantum computing. Collaborate with interdisciplinary research teams to integrate AI/ML capabilities into scientific simulation, data analysis, and computational workflows. Contribute to the development and
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including tensile, fatigue, creep, impact, and nanoindentation testing. Analyze lattice strain evolution, phase stress, and defect evolution using advanced data analysis tools. Perform alloy fabrication and
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optimization, and application-driven performance analysis for HPC, scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature
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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
familiarity with AI/ML algorithms, for generative materials design, or for knowledge extraction, e.g. causal ML or symbolic regression, etc. Strong demonstrated background in coding for data analysis using