77 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Argonne
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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density
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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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campus in Lemont, Illinois five days per week. Preferred Qualifications Proficiency in programming (e.g., Python) for advanced data analysis, machine learning, and computer vision to accelerate insights
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in computational science, machine learning, and experience with synchrotron data analysis are strongly encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference
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, computational physics, computational materials science, inverse problems, signal processing, x-ray science etc. are encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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artificial intelligence/machine learning (AI/ML). The successful candidate will contribute to the group’s broad physics program, which includes precision Higgs and Standard Model measurements, and searches
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++, or similar, with experience in data-driven workflows and computer vision Demonstrated track record of peer-reviewed publications Highly collaborative, innovative, and capable of working independently in a
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer