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in deep learning at scale, familiarity with the “alphabet soup” of distributed computing (DP, TP, SP, CP, EP) Experience with production environments, including Git-based workflows Experience working
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). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance portability programming language (e.g., CUDA, HIP, Kokkos, SYCL
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environments, parallel and distributed file systems, tiered storage strategies, and hybrid cloud integration. Strong communication skills across technical and non-technical audiences Strong understanding
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of cluster and job/resource management software (e.g., Warewulf, Slurm, XCat) Parallel and distributed file system (Lustre) experience is a plus Experience with the installation, configuration and
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Qualifications Experience: Relevant programming experience developing, implementing, debugging, and maintaining applications with Python. Experience working with high performance computers (e.g., parallelizing and
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given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel
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physics Physics » Thermodynamics Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Application Deadline 19 Apr 2026 - 23:59 (Europe/Brussels
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State University of New York University at Albany | Albany, New York | United States | about 2 hours ago
vibration isolation). In parallel to the benefits of quantum computing, artificial intelligence and neuromorphic computing seek to emulate the massively parallel, highly efficient computing capacity
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large-scale scientific research and foundation models. The Machine Learning Science Cloud is part of the AI/ML compute ecosystem in Tübingen. Our users work on diverse research and transfer projects
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at least one of: High-Performance Computing Distributed Systems Parallel Computing High Performance Algorithms Multiple Linux distributions · Experience with SLURM or similar HPC schedulers