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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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The Q-NEXT National Quantum Information Science and Research Center based at Argonne National Laboratory invites applications for a postdoctoral position to conduct research in the field of material
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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research. Responsibilities : Develop and apply machine learning and data-driven methods for the analysis and interpretation of synchrotron X-ray scattering data, including diffraction and total scattering
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physics, etc. Proficiency in Python or other scientific programming languages. Programming skills in numerical methods for image processing and AI/ML methods for quality improvement are advantageous
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to current MSR developers for use in developing molten salt fuel specifications. Develop novel and improved methods for measurements of molten salt properties and standardize procedures to ensure quality data
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multidisciplinary team, the candidate will work at the intersection of AI/ML, domain sciences, and high-performance computing. The role requires a strong foundation in LLMs and machine learning, along with
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The Multiphysics Computations Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing high-fidelity scale-resolving computational fluid dynamics (CFD
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, co-simulation, or hardware-relevant environments. Primary Responsibilities: Develop machine learning and AI methods for control, optimization, and cyber-resilient operation of distribution systems, DER
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be