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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 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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Infrastructure Sciences Division. Machine learning (ML), specifically deep learning (DL), has been demonstrated to successfully predict the weather for 1-14 days with skill on par with numerical weather prediction
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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fabricate nanoscale electrical test structures (e.g., photolithography, e-beam lithography) Design, test, and characterize radiofrequency (RF) circuitry and measurement approaches Analyze and interpret data
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fabrication, including mask design, metal sputtering, laser or e-beam lithography, etching, and device integration Familiarity with thin-film materials (e.g., oxides, dielectrics, metals) and their integration
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devising and performing experiments to acquire data, using and maintaining research equipment and instruments, compiling, evaluating and reporting test results. Knowledge and experience in chemical
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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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. 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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microelectronics project. To learn more: Argonne to lead two microelectronics research projects under U.S. Department of Energy initiative | Argonne National Laboratory Position Requirements Recent or soon-to-be