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Field
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appropriate use of GPU resources. Digital cultural heritage: OCR and HTR, enrichment of historical and multimodal collections, and applied studio courses delivered with GLAM partners. This list describes
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | 2 months ago
, operate, and scale HPC clusters and GPU resources to support computational biology, genomics, and machine learning workloads. Manage virtualization and containerization platforms (VMware, KVM, Docker
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projects, deploying workflows on large-scale computing environments, leveraging ORNL’s Frontier for its dense GPU-based high-performance computing resources to train large GeoAI models. Major Duties
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configuring GPU-enabled systems for deep learning tasks. o Network Administration: Strong understanding of networking concepts, including TCP/IP, routing protocols, VLANs, and Layer 2 switching. Experience with
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modeling, multi-modal drug discovery, retrosynthesis, and AI for science more broadly. SPARC offers a collaborative, computationally rich environment with GPU/HPC infrastructure, large biomedical data assets
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dedicated GPU-equipped computing workstation and data-storage resources; - access to molecular-modeling and chemoinformatics software; - resources for the acquisition and experimental evaluation
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, and maintaining automated data pipelines for large-scale time-series or imaging datasets. Experience with HPC/cluster computing environments, including SLURM job scheduling and GPU-accelerated
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, especially for GPUs across multiple hardware vendors, as well as experience in software sustainability and design patterns is expected. Major Duties/Responsibilities: Collaborate within a multi-disciplinary
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, and maintaining automated data pipelines for large-scale time-series or imaging datasets. Experience with HPC/cluster computing environments, including SLURM job scheduling and GPU-accelerated
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-on experience in one or more of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high