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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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become part of a new holotomography software package actively being developed by the team. Position Requirements Required Knowledge, Skills, and Experience: PhD (recently completed or soon-to-be completed
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. Contribute to open-source software development initiatives for Department of Energy projects. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years in
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We are seeking a Postdoctoral Appointee to work in the Mathematics and Computer Science (MCS) Division of the Computing, Environment, and Life Sciences directorate (CELS) of Argonne National
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well as present findings to the community through publications and presentations. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in chemical engineering
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related field at the PhD degree level with zero to three years of experience or equivalent in the scientific application of this knowledge and practical laboratory experience. To perform the essential
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is typically achieved through a formal education in chemical engineering, chemistry, materials science, nuclear engineering, mechanical engineering, or related field at the PhD degree level with zero
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to develop, synthesize, characterize and electrochemically evaluate next generation cathode materials for lithium-ion and sodium-ion batteries. Position Requirements Recent or soon-to-be-completed PhD
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achieved through a formal education in chemical engineering, mechanical engineering, or a related field at the PhD degree level with zero to five years of experience or equivalent in the scientific
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing