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people and cultures for the betterment of humanity. KAUST offers superb research facilities, including the Shaheen supercomputer (listed as top 20 in the HPC world list) sporting 3000+ GPUs; generous
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computing (HPC) systems, including CPU, GPU, storage, file systems, networking, visualization, job schedulers, and scientific applications Experience leading the implementation and execution of research
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are looking for candidates with experience in ML model deployment, workflow orchestration, and high-throughput data processing, as well as experience working with large biological datasets in GPU-based
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the College of Engineering. UNLV GPU Cluster (named RebelX) is also available for A.I. research and education. Detailed information about the CEEC Department can be found at: http://www.unlv.edu/ceec MINIMUM
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when necessary. Work closely with ODFM to facilitate request for office and lab access. Manage any IT matters, including IT equipment allocation and support for GPU servers (if required). 2. Support on
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, versioned RESTful API (e.g., FastAPI/Flask) for inference; and Containerise services (Docker), set up CI/CD pipelines, and deploy for inference on GPU/CPU servers. Data engineering, Governance, and
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(HPC) or GPU-based workloads preferred Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch). Strong proficiency with containerization (Docker), orchestration (Kubernetes), and
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AI pipelines (similar to the attached prototype). Enable users to configure model parameters, connect modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU
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performance computing systems or cloud infrastructure (including GPU-accelerated workloads). Practical experience with modern deep learning frameworks, model serving in production, and building end-to-end data
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. Responsibilities Drive innovative research on biomolecular condensates and develop advanced models for phase separation using molecular dynamics. Optimize performance by running large-scale and GPU-accelerated