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to effectively communicate their results and insights to both scientific peers and broader audiences. Furthermore, maintaining awareness of state-of-the-art techniques and expertise within related research groups
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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mechanistic insights, and communicate scientific findings through technical reports, peer-reviewed publications, and conference presentations. Collaborate with multidisciplinary research teams across Argonne
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-friendly software interfaces that enhance data accessibility of data and insight for diverse stakeholders. Facilitate ongoing communications and foster relationships with a broad array of stakeholders
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systems, including oxides, composites, and electrochemical interfaces, with the ability to apply insights from related research areas to solve battery materials challenges Demonstrated ability to process
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, including cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and galvanostatic cycling Analyze and interpret experimental data to provide insight into battery mechanisms and performance