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-processing, information extraction, and post-processing using existing SDR frameworks and algorithms Rapidly prototype SDR signal chains and systems through design, programming, hardware integration, testing
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for unstructured meshes and/or finite element methods Experience with CFD discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC
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for unstructured meshes and/or finite element methods Experience with CFD discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC
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research associate position in AI for science. The Learning Systems Group seeks a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms. The successful candidate will
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instrumentation Strong programming experience for developing custom algorithms (knowledge of programming in LabVIEW and Python environments), along with experience in AI/ML tools and workflows, including training
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and
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travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding
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uncertainty quantification. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and
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algorithms, capable of distributed learning on high performance and edge computing; The design of architectures/models which accurately capture the complexities of the data, with robust estimates of confidence