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                The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral researcher position in the field of hybrid quantum computing. This exciting project 
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                The Center for Nanoscale Materials (CNM) at Argonne National Laboratory is seeking postdoctoral researchers to work on distributed quantum computing. The project aims to develop superconducting 
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                We are seeking a highly motivated Postdoctoral Researcher with expertise in computational biology, deep mutational scanning data, and generative artificial intelligence (AI). The successful 
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                modeling is critical Considerable computational expertise in using quantum mechanical methods to calculate reaction mechanisms and kinetics in heterogeneous systems is essential Ability to program in C++ and 
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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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                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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                at technical conferences. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, computer 
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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 
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                -Informed Neural Networks (PINNs) and geometric deep learning. Experience with active learning, agentic workflows, or other methods for autonomous experimentation. Familiarity with high-performance computing 
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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