85 computational-physics-"https:"-"https:"-"https:"-"https:"-"IFM" positions at Argonne
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Applications are invited for post-doctoral positions in the Cosmological Physics and Advanced Computing Group (CPAC) Group in Argonne National Laboratory’s High Energy Physics (HEP) Division
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The Materials Science Division (MSD) of Argonne National Laboratory is seeking applicants for a postdoctoral appointee in physics of colloidal systems. The postdoc is expected to conduct research
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Argonne National Laboratory is seeking a Postdoctoral Appointee to work in the Advanced Energy Technologies (AET) Directorate. The successful candidate will study chemical upgrading of heavy oils, asphaltenes, resins, and kerogen molecules and contribute to engineering design of upscaled...
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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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lead a world-class research program that aligns with DOE priorities in low-energy nuclear physics outlined in the 2023 Nuclear Science Advisory Committee Long Range Plan for Nuclear Science. Additional
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The Materials Science Division (MSD) of Argonne National Laboratory is seeking applicants for a postdoctoral appointee in experimental condensed matter physics. Although exceptional candidates in
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looking for candidates whose research program aligns with the 2023 Long Range Plan for Nuclear Physics, focusing on lab-based tests of fundamental symmetries via precision experiments. The ideal candidate
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Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow
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at conferences and ALCF/DOE venues. Position Requirements Required Skills and Qualifications: Ph.D. in Computer Science, Physics, Chemistry, Biology, Engineering, Mathematics, or a related computational discipline
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Because of the drastically increasing demand from AI/ML applications, the computing hardware industry has gravitated towards data formats narrower than the IEEE double format that most computational