91 computer-security "https:" "https:" "https:" "https:" "https:" "https:" "U.S" research jobs at Argonne in United States
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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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instrument proposed under a DOE Major Item of Equipment (MIE) effort. Building on two decades of APS XRS capability (including the LERIX program at 20-ID) and recent commissioning work at Sector 25
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materials in engineering controls such as gloveboxes or hoods is desired, but not mandatory. Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork. To perform the essential
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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models for microelectronics materials Curate, manage, and integrate heterogeneous datasets from experiments and simulations Collaborate closely with experimental teams to benchmark and refine computational
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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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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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, interdisciplinary environment with access to large-scale computing resources and diverse scientific use cases. The position strongly supports publishing in top-tier venues, contributing to open-source research
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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multidisciplinary team, the candidate will work at the intersection of AI/ML, domain sciences, and high-performance computing. The role requires a strong foundation in LLMs and machine learning, along with