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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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Publish scientific results in peer-reviewed journals in a timely manner Participate in project planning and execution Maintain detailed and accurate records Acquire and analyze data in keeping with project
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful
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, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD degree in Mechanical
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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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another, work together, and measure success. Basic Qualifications: A PhD related to computational or theoretical condensed matter physics, theoretical chemistry, theoretical materials science, or other
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. Basic Qualifications: A PhD in Materials Science & Engineering, Physics, Chemistry, or a related field completed within the last 5 years A minimum of 2 years of post-Ph.D. experience utilizing
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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