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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials Expertise with artificial intelligence and machine learning approaches will be also
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage. Proficiency in materials data analytics, including correlation analysis and machine learning techniques. Preferred
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
another, work together, and measure success. Basic Qualifications: A PhD in Physics, Materials Science, Chemistry, or closely related field completed within the last 5 years. Sound understanding of advanced