86 embedded-system-"https:"-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions at Argonne in United States
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calibrated ensemble system for S2S at high resolution (30-km) to deliver probabilistic weather forecasts beyond 14 days to allow for actionable, local-scale impacts on infrastructure and communities. The ideal
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Strong data analysis skills: familiarity with modeling and/or data-science tools is beneficial but not required Excellent written and verbal communication skills and the ability to work effectively in a
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models for high-temperature structural materials with applications in nuclear reactors and other energy systems. The candidate will collaborate with ANL staff to review, validate, and enhance methods
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of radiofrequency (MHz–GHz) nanoscale phenomena in systems relevant to microelectronics and quantum information science. Opportunities also exist for cross-platform studies integrating ultrafast TEM with ultrafast x
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hardware. Duration of the appointment is one year initially and renewable for up to three years contingent on performance and funding. The successful candidate will be supported by ALCF's Performance
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The Materials Science Division (MSD) at Argonne National Laboratory is seeking a postdoctoral appointee to join the Nanoscale Magnetic and Electronic Heterostructures group. This position will focus
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The Chemical and Fuel Cycle Technologies division is seeking a Postdoctoral Appointee to join a multidisciplinary team developing processes to support molten salt reactor (MSRs) fuel cycles
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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The Coherent and Ultrafast X-ray Science Group in the Materials Science Division at Argonne National Laboratory is seeking a highly motivated postdoctoral appointee to lead research in ultrafast
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing