91 assistant-professor-computer-science "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" Postdoctoral positions at Argonne
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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
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Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow
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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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contribute to open-source code repositories and documentation. Position Requirements Required skills, knowledge and qualifications: PhD in physical oceanography, coastal engineering, computational science
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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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/reactions, with increasing emphasis on using artificial intelligence and quantum information science. The group has access to extensive laboratory and national computational resources and has significant
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primary goal of this work is aimed at advancing next-generation, lithium-ion technology through a detailed understanding and mitigation of surface degradation mechanisms that limit state-of-the-art lithium
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Recent or soon-to-be completed (typically within the last 0-5 years ) Ph.D. in Computer Science, Electrical Engineering, or a related field. Demonstrated research expertise in AI and machine learning, with
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have a strong background in fundamental electrochemistry, with preferable hands-on expertise in computational materials science. The applicant should be well versed in code development, application of AI
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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