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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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for quantum information science, but many open questions remain regarding how to control the morphology and crystallinity of these host materials for exemplary performace as hosts for optically addressable spin
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and processing strategies aimed at achieving high performance, cost-effectiveness, and manufacturability. The selected candidate will leverage the capabilities of the Materials Engineering Research
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The Argonne Leadership Computing Facility (ALCF) is dedicated to advancing scientific discoveries and engineering breakthroughs by providing world-class computing facilities in collaboration with
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engineering, or a related field. Demonstrated expertise in process modeling, process optimization, and technoeconomic analysis. Programming skills in scientific computing languages such as Python and Julia
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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, project presentations, and other regular channels. Position Requirements Skill in modeling, processing, and analyzing computational results to inform accompanying experimental efforts. Skill in the use
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and contract. Skill in modeling, processing, and analyzing computational results to inform accompanying experimental efforts. Skill in the use of modern collaborative coding practices. Demonstrated
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The Computational Science Division (CPS) at Argonne National Laboratory (near Chicago, USA) is seeking a postdoctoral researcher to enable exascale atomistic simulations of ferroelectric devices
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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