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materials from complex feedstocks to achieve the desired product quality and form. As a part of this team, you will : Apply electrochemical engineering principles to develop processes such as oxide reduction
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microbial communities play a fundamental role in carbon and nutrient cycling, yet their spatial organization and interactions have remained difficult to study because of the opacity and complexity of soil
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appropriate, experimental apparatus Prepare and characterize materials using laboratory-based analytical techniques Analyze and interpret complex, multimodal experimental datasets Communicate research results
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. Responsibilities include planning experiments, analyzing complex datasets, developing mechanistic understanding of material behavior, publishing research findings in high-impact journals, presenting results
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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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learning (ML) to address future physics and detector challenges. Current physics interests include Standard Model measurements and searches for new phenomena. We welcome applicants who are excited
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an excellent team player. A high commitment to safety. Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork. This position requires an on-site presence at the Argonne campus
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organized notebooks and project files. Proven ability to be an excellent team player. A high commitment to safety. Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork
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. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred skills: Microwave circuit design, terahertz optics, and their characterization. Knowledge about near
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that influence the development, intensification, and persistence of extreme events, using observational datasets, machine learning, and Earth system modeling. The successful candidate will work with observational