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Tech. ARC currently operates compute resources comprised of • 50,000+ CPU cores • 500+ GPUs • over 10 PB of storage • a world class immersive visualization lab. We continuously evaluate emerging research
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modules, AWS, Azure, GCP, or comparable services. Experience supporting GPU, AI/ML, CPU-intensive, statistical, imaging, compiler/toolchain, Fortran, MATLAB, R, Python, CUDA, or other discipline-specific
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computing pipelines and large-scale GPU resources, to scale LLM development and deployment. Your profile PhD in machine learning, computer science, computational biology, computational neuroscience
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machine learning frameworks (e.g., TensorFlow, PyTorch). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications: Experience with multi-GPU model training and
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, reproducibility, and the traceability of evidence develop project- and problem-based teaching in which students from different disciplines work together with authentic research data and questions supervise and
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, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This position is a prestigious Empire AI Fellowship
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innovative employers in the region. With more than 6000 employees from 100 different countries, we are helping to build tomorrow's world every day. Through top scientific research, we push back boundaries and
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Processing Unit (GPU) hardware. Working Conditions Needs to be able to successfully perform all required duties. Office/research environment; some travel and weekend work is required. UTRGV is a distributed
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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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the most important, as a small change can be the difference between normal and pathological. We want to understand this bias and correct it. This is a fully funded position (TV-L E13, 100%) for a PhD student