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developing and managing automated and parallel scientific workflows , including task execution systems, dependency management, and pipeline execution on distributed computing resources. Experience with HPC
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benchmarking of parallel and distributed computing systems and of the workflow systems that run on them. Familiarity with the FAIR data and research software principles, and with producing AI-ready
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, Electrical Engineering, Mathematics, or a closely related field. Strong programming proficiency in Python and C or C++. Experience with parallel and distributed computing using technologies such as MPI
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workflows, and 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
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leading the design and deployment of HPC or large-scale distributed computing systems. Expertise with batch schedulers (SLURM, PBS, LSF) and parallel file systems (Lustre, GPFS/Spectrum Scale). Proven
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distributed computing runtime with application and compilers to efficiently integrate quantum processors with HPC resources. The research will explore different quantum computing modalities, their
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systems for extreme-scale, heterogeneous computing. This role sits at the intersection of compiler infrastructure, formal verification, runtime systems, and hardware/software co‑design—expanding into
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systems for extreme-scale, heterogeneous computing. This role sits at the intersection of compiler infrastructure, formal verification, runtime systems, and hardware/software co‑design—expanding into
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on high-performance computing (HPC) environments using distributed data or model parallel training. Experience with multi-physics simulations on HPC and with ML models. Experience working in a multi
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workflows, including parallel and distributed applications, data-intensive pipelines, and accelerator-enabled workloads. Work closely with system administrators, infrastructure engineers, and researchers