37 programming-"https:"-"https:"-"https:"-"https:"-"IDAEA-CSIC" Postdoctoral positions at Argonne
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completed within the last 0-5 years)in chemical engineering, materials science, industrial engineering, or related fields. Knowledge of Python, JavaScript, Microsoft Excel and other computer programming
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operando studies Skills in sample synthesis and preparation Familiarity with CAD software, 3D printing, and programming for data analysis Strong written and verbal communication skills, including a record of
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and engineers across Argonne, including the Materials Engineering Research Facilities (MERF) and the Argonne MXene Innovations (AMI) program, while collaborating with industrial and academic partners
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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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learning, or optimization Strong programming skills in Python and experience with scientific computing and machine-learning libraries Ability to work across experimental, robotic, and computational systems
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The Argonne National Laboratory at U.S. Department of Energy supported research programs invites applications for a Postdoctoral Research Associate position focused on understanding the relative
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is supported by a DOE-funded research program on ultrafast science involving Argonne National Laboratory, University of Washington, and MIT. The goal of this research program is to understand and
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well as in industry and at other national laboratories. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years)in Engineering or similar program. At least 2
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and machine learning techniques in analyzing supply chain risk. Ability to develop and synthesize visualizations to effectively communicate analysis results. Strong programming skills in statistical
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2.0) program. The collaboration team includes Clarence Chang, Tim Hobbs, Dafei Jin, Yi Li, Marharyta Lisovenko, Valentine Novosad, Zain Saleem, Tanner Trickle, and Gensheng Wang. We seek highly