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to analytical techniques for characterizing effluent analytes using LC-MS, GC-MS, IC, ICP-MS, including developing in situ measurement routines aimed at understanding degradation mechanisms of membrane and
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The ALCF has an opening for a postdoctoral position in AI-assisted scientific visualization and intelligent data exploration. The successful candidate will join the Visualization and Data Analytics
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increasing independence in defining research problems, developing methodologies, and driving publications. The role values strong research judgment and analytical thinking, complemented by effective use
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energy supply chain strategies. Apply advanced analytics and methods to analyze trade, production, and geopolitical data to identify risk in critical supply chains. Develop and maintain analytical models
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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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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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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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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