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We are seeking creative Postdoctoral researchers to bridge the gap between leadership-class supercomputing and cutting-edge open science in the area of AI and Machine Learning. Successful candidates will be working with ALCF’s technical teams (e.g., AI/ML, Data Science, Performance Engineering)...
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We are seeking a highly motivated and creative Postdoctoral Researcher to join the X-ray Science Division (XSD) at Argonne National Laboratory. The successful candidate will develop web-based AI
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++, or JavaScript Experience with AI or machine learning techniques, including large language models or agentic systems Experience developing or integrating interactive visualization systems, including web-based
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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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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
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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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to/from the memory via optical fibers. The candidate will be primarily responsible for: (1) advancing our experimental program to fabricate new hybrid devices in Argonne’s Center for Nanoscale Materials
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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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, Quantum Information and Quantum Simulation. The successful candidate will be expected to carry out an independent and collaborative research program in particle theory that strengthens and complements
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Plan and execute in situ/operando experiments using advanced characterization methods, including Near Ambient Pressure X-ray Photoelectron Spectroscopy (NAP-XPS), electron microscopy, Raman spectroscopy