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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed
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distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute to cutting-edge research aimed at transforming scientific data management and
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efforts across scientific systems. Develop and apply federated learning on distributed and heterogenous datasets. Develop more efficient and resilient DP techniques that minimize performance loss while
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single node between multiple secure workloads. Investigate and evaluate mechanisms for secure encrypted communication across RDMA based networks. Design and evaluate key distribution and management
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simulation tasks. Develop and maintain HPC-ready software workflows for distributed training, large-scale inference, scalable data ingestion, and data management on leadership-class computing systems and
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), custom accelerators, and next-generation quantum and analog devices. Your work will contribute to a vision of autonomous system engineering in which AI agents understand algorithmic intent, architectural
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-based or LiDAR-inertial SLAM, high-accuracy point cloud mapping, or scan-to-scan registration. Experience with ROS 2 navigation stack, lifecycle-managed nodes, TF2, rosbag workflows, and distributed
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-generation, data-driven manufacturing systems that integrate artificial intelligence, real-time sensing, and digital twins to transform how critical components are designed, produced, and qualified
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. Demonstrated programming ability and knowledge of Python and/or C++. Experience with deep learning frameworks like PyTorch and application on high-performance computing (HPC) environments using distributed data