94 engineering-computation "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at Argonne in United States
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Physics, Materials Science, Chemistry, Chemical Engineering, Applied Physics, or a closely related field with a focus on computational materials modeling. Density Functional Theory (DFT) for surfaces and
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, Chemical Engineering, Electrical Engineering, or a related field Proven research track record in computational materials science and AI/ML, with applications in areas such as quantum information
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engineering principles Experience working safely with hazardous materials using engineering controls such as gloveboxes is desired. Knowledge of the use of computers to design and control experiments and to
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The Cosmological Physics and Advanced Computing (CPAC) group at Argonne National Laboratory invites applications for a postdoctoral researcher to work closely with Dr. Lindsey Bleem
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The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge computational and systems biology research. The primary focus
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forces to tackle some of the most challenging problems in science and engineering. Fellow has access to world-leading extreme-scale computing resources and experimental facilities. We strongly value
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of funds. Relevant Publications: 1. P. Chen et al ., Ultrafast photonic micro-systems to manipulate hard X-rays at 300 picoseconds, Nat Commun, 10:1158 (2019). https://doi.org/10.1038/s41467-019-09077-1 . 2
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The Advanced Photon Source (APS) (https://www.aps.anl.gov/ ) at Argonne National Laboratory (Lemont, Illinois, US (near Chicago)) invites applicants for a postdoctoral position to build a physics
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral researcher position in the field of hybrid quantum computing. This exciting project
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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with