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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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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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Fabry-Perot interferometry, optical frequency-domain reflectometry, optical time-domain reflectometry, or distributed acoustic sensing. Proficiency in Multiphysics modeling software (ANSYS, COMSOL, MOOSE
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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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distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross‑Paradigm Integration(new optional emphasis): Explore how
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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