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multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists, with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with
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techniques in interfacial science; and mathematical techniques and computer programming for data analysis. Considerable skill in working interactively and productively in a multidisciplinary environment Good
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experience: Familiarity with machine learning hardware Familiar with high performance scientific infrastructure Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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. Develop advanced optimization, control, or machine learning strategies for distribution systems; validate these strategies using hardware-in-the-loop or real-time grid simulators. Develop optimization
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++, or similar, with experience in data-driven workflows and computer vision Demonstrated track record of peer-reviewed publications Highly collaborative, innovative, and capable of working independently in a
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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with a team. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience in machine learning/deep learning methods
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modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks
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, inclusive, and accessible environment where all can thrive. Additional Preferred Qualifications: Working knowledge of power system protection and control. Familiarity with Machine Learning. Familiarity with