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dedicated computing resources (3D visualization workstations, GPU servers). The research will be conducted in close collaboration with Philippe Nghe's team (Laboratory of Structural Cell Biology at École
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Training and optimizing ML algorithms on GPU hardware architectures, specifically NVIDIA based Working with geo-spatial data Statistics, multivariable calculus, and linear algebra Exploratory data analysis
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preferred). Familiarity with CUDA toolchains and building GPU-enabled software. Experience with automation/configuration management tools (e.g., Ansible, Puppet, or similar). Experience developing tools
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managing systems utilizing GPU (NVIDIA and AMD) clusters for AI/ML and/or image processing. Knowledge of networking fundamentals including TCP/IP, traffic analysis, common protocols, and network diagnostics
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collaboratively in a research-focused environment. Project background We train open foundation models with hundreds of billions of parameters on thousands of GPUs on one of the largest AI-ready supercomputers in
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track record of open-source contributions. Project background We train open foundation models with hundreds of billions of parameters on thousands of GPUs on one of the largest AI-ready supercomputers in
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have access to substantial AI compute, including in-house state-of-the-art H200 GPU servers, alongside further capacity through the Norwich Data Centre and access to national-scale AI compute through
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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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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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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