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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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linear programming, mixed-integer programming, nonlinear optimization, stochastic optimization, robust optimization, or multi-objective optimization. Experience integrating machine learning or data-driven
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programming and data analysis (Python, MATLAB, or equivalent). Experience with machine learning, data-driven modeling, or AI methods applied to physical sciences datasets. Familiarity with handling and
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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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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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-completed PhD (within the last 0-5 years) in field of physics, engineering, or a closely related field Demonstrated programming proficiency in C/C++, Python, or another scientific programming language
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of colloidal systems; optical microscopy techniques Optical microscopy Image and data processing Basic programing for experiments Image and data processing/analysis Please upload the following as attachments
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completed in a relevant field of study. Experience with deep learning (DL) frameworks such as PyTorch, TensorFlow, or JAX. Strong programming proficiency in Python. Demonstrated experience with coherent X-ray