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, computational physics and x-ray science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of the world’s largest supercomputers (Polaris
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prototype, benchmark, and evaluate strategies to better support these workloads for Aurora. Position Requirements Required skills and qualifications: A recent PhD (within 5 years) in computer science
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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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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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of impact, safety, respect, integrity, and teamwork. This level of knowledge is typically achieved through a formal education in materials science, physics or related discipline at the PhD level or
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Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical/aerospace engineering, chemical engineering, or a related discipline. Onsite presence is required
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(predoctoral) or PhD (postdoctoral) in Materials Science, Chemistry, Physics, or related area is required. Coursework in computer science or data science is desirable. Familiarity with research data management
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policy, environmental science, or a related field at the PhD level with zero to five years of employment experience. Technical background in economics with a focus on the mineral and energy sectors. Proven
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your PhD in computer science or engineering, the physical sciences, or a related field within the last five years. Comprehensive programming proficiency, preferably in Python. Experience with machine
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nanofabrication. Experience in microwave and optical device characterization and measurement. Knowledge and good understanding of quantum information science. Experience with superconducting qubit design