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optimization, with experience in adaptive routing and SDN technologies. Proficiency in programming languages such as Python, C/C++, and experience with parallel computing frameworks. Effective written and oral
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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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) Proficiency in programming languages such as Python or C++ Experience with AI frameworks like PyTorch or TensorFlow Strong communication skills and ability to work in a team environment Ability to model
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran
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turbulent combustion applications, as well as parallel scientific computing. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and
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for developing new computational tools and AI/ML approaches to analyze and correlate data from multiple imaging modalities, including synchrotron tomography, x-ray fluorescence microscopy, visible light microscopy
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development. Knowledge of parallel scientific computing. Ability to meet project needs and tight deadlines. Present and publish results in peer reviewed papers and/or journal articles. Skilled verbal and
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open access of datasets. Key Responsibilities Develop and implement data management strategies to support research activities across multiple institutions. Collaborate with researchers to establish data
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that can process and learn from multiple data streams in real-time is key to unlocking the full potential of such instruments. The postdoctoral appointee will be responsible for developing such methods
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multiple groups within the X-ray Science Division, the Center for Nanoscale Materials and the Materials Science Division of Argonne. Position Requirements Ph.D. in material science and engineering, physics