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addition to conventional tools such as XRD, BET, XRF, SEM, TEM, etc. Collaborate with computational modeling and artificial intelligence/machine learning teams to improve battery performance, using rational design
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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
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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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encompass: Catalysts Synthesis: Utilize your expertise in materials synthesis to develop novel catalysts guided by machine learning algorithms Catalyst Performance Evaluation: Utilize aqueous electrochemical
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researchers performing molecular modeling and machine learning activities. The candidate will be expected to perform ion conductivity experiments with thin film polymer electrolytes. The candidate will use