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in artificial intelligence and machine learning, particularly for control applications Proficiency in Python Strong analytical and problem-solving skills Ability to work independently and
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The Energy Systems and Infrastructure Assessment (ESIA) division at Argonne provides the rationale for decision makers to improve energy efficiency. ESIA develops and uses analytic tools to help
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streams to products. An understanding of process scale-up and chemical plant design is of importance as well. The candidate should be familiar with databases and analytical models relevant to process
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design, advanced modeling and high-performance computing, mathematics and data analytics, AI/ML algorithm development, and accelerator operations Ability to model Argonne’s core values of impact, safety
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techniques and analysis of large, multimodal datasets Experience in materials synthesis and materials characterization Strong foundation in general analytical methods and laboratory practices Excellent oral