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skills and qualifications: A recent PhD (completed within the last 5 years) in computer science, electrical engineering, or a related field. Strong background in network interconnect design and
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modeling tools to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
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The Chemical Sciences and Engineering Division is seeking applicants for a postdoctoral appointee who will conduct computational research in Selective Interface Reactions (e.g., atomic layer
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We invite applications for a Postdoctoral Appointee to contribute to a growing research program in process systems modeling and optimization for clean energy, critical materials, and advanced
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and methods, fostering innovation and accelerating progress in the development of efficient solar energy conversion technologies. Position Requirements Recent or soon-to-be-completed PhD (within
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We are seeking a Postdoctoral Appointee to work in the Mathematics and Computer Science (MCS) Division of the Computing, Environment, and Life Sciences directorate (CELS) of Argonne National
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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-house codes and making use of high-performance computing (HPC) tools. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical, aerospace
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The Nuclear Science and Engineering (NSE) Division is seeking a postdoctoral appointee to develop computational methods and computer codes to model the physics and engineering of advanced nuclear
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the Scaling Machine Learning (SML) effort of the High-Energy Physics Center for Computational Excellence (HEP-CCE), the candidate will be responsible for facilitating the scaling of HEP ML workflows