82 web-programmer-developer-"LIST" "https:" "https:" "https:" "https:" "University of Southampton" Postdoctoral positions at Argonne
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental
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The Materials Science Division (MSD) at Argonne National Laboratory is seeking highly motivated applicants for a postdoctoral appointee to join a multidisciplinary team developing next-generation
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structural models and compute electronic and vibrational properties. Develop and train neural-network or other machine-learned interatomic potentials to enable large-scale molecular dynamics (MD) simulations
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The Chemical and Fuel Cycle Technologies division is seeking a Postdoctoral Appointee to join a multidisciplinary team developing electrochemical reactions and processes in molten salt electrolytes
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The Microscopy group in X-ray Science Division of Advanced Photon Source at Argonne National Laboratory is seeking postdoctoral researchers to work on cutting-edge ptychography technique development
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through the design and synthesis of nanoscale epitaxial oxide thin films. This position supports a three-year Laboratory Directed Research and Development (LDRD) project focused on developing scalable
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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
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material property database for composites. The candidate will utilize the database to develop AI models for composite discovery. The candidate will work with a multidisciplinary team to set up finite element
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. Working within an interdisciplinary team, you will develop frameworks that connect atomistic features, mesoscale dynamics, and device-level performance. The effort will integrate heterogeneous data from
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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