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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
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis. Apply artificial intelligence, machine learning, LLMs, and advanced statistical
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team and actively interact with CNM and Argonne scientists as well as external collaborators. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of experimental
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PhD (within the last 0-5 years) in chemistry, physics, chemical engineering, or a closely related field A strong foundation in quantum mechanics and electronic structure theory Demonstrated expertise in
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of analytical and electrochemical tests for new battery electrolyte materials. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in Chemistry, Materials
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initiatives, providing valuable insights to peer reviewers and program managers alike. Position Requirements Required skills and qualifications: Recently completed PhD within the last 0-5 years in an applicable
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multidisciplinary research team Communicate research findings clearly through written reports, presentations, and publications Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in
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will require flexibility and a willingness to learn new techniques and approaches. In addition, there may be overnight experiments being run unattended, the candidate must be able to respond to issues in
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to contact Peter Winter ([email protected]) or Nazar Delegan ([email protected] ) for more details and questions on the position. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5