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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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experiences on computer vision • Strong programming skills in using Deep Learning tools like PyTorch and GPU clusters. • Good written and verbal communications. • Open to Fixed Term Contract
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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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, e.g., git, automated testing, packaging, documentation and code review. Experience with GPU computing or high-performance computing. Interest or experience in LLMs, tool-using agents, or agentic
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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
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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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, or Transformer-based architectures Familiarity with training and evaluating models on GPU-accelerated hardware Ability to build reproducible experiments and maintain clear documentation Integrating AI/ML models
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of environmental factors of 60,000 subjects across multiple time points. Our research laboratory has great computing capacity, including multiple H100 and A100 GPU systems for deep learning, and computing clusters
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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