62 experiment-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions at Argonne
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design and conduct experiments, interpret results, analyze data, and communicate findings through presentations at scientific conferences and publications in peer-reviewed journals. Key Responsibilities
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The researcher will work at the intersection of materials science, chemistry, data science, and laboratory automation to identify promising materials, guide experiments, and establish relationships
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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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and structural behavior of these strategically important elements in geological materials. We are looking for a creative and driven experimental scientist with experience in synchrotron scattering and
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, including AI agents that can assist with task orchestration, experiment planning, model evaluation, workflow automation, and decision support across distributed environments. Build and extend software
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, Chemical Engineering, Mechanical Engineering, Materials Science, and/or Electrochemistry Demonstrated experience in electrochemical testing and physicochemical characterization of lithium- and/or sodium
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. Experience developing, manipulating, and analyzing large data sets. Understanding of energy technologies and their supply chains. Experience working with Argonne’s R&D GREET model and BatPaC battery cost model
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solid electrolytes. We are seeking candidates who will be able to design experiments and develop methodologies to design material compositions, ink rheological properties, and coating and drying process
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field. Strong computational chemistry background in atomistic simulations, electronic-structure theory, DFT, structure-property relationships, and interpretation of simulation results. Hands-on experience
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experience Strong background in atmospheric dynamics, turbulence, land–atmosphere interactions, cloud physics, or precipitation processes Experience working with large observational or model datasets