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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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on high performance computing systems or cloud infrastructure (including GPU-accelerated workloads). Practical experience with modern deep learning frameworks, model serving in production, and building end
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resources of TU Delft, ranging from personal machines, to shared GPU servers, the Delft AI Cluster that is shared across departments, as well as DelftBlue , which is one of the top 250 supercomputers in
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compute hardware and network infrastructure for the ITS Research services. This covers the 12000+ core, 100+ GPU QM High Performance Compute cluster (see https://docs.hpc.qmul.ac.uk ), various hosted
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, OpenCV), GPU computing (e.g., CUDA), SLAM and point cloud processing (e.g., PCL). Familiarity with ROS-based development and Linux software tools is considered a fundamental asset. (30
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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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on GPU clusters and cloud HPC/ADK Implement data-efficient fine-tuning, adaptive learning workflows and agentic frameworks for reasoning Collaborate with machine learning experts and computational
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algebra methods targeting large-scale HPC systems. Optimization of linear algebra libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications
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and diffusion models, LLMs, VLMs, LAMs, and world models, and fluency in tools for AI/real-time/graphics pipelines (e.g., Python, PyTorch, C++, GPU/compute, networking). Base location: Pinewood Studios
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models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization. Experience in multi-modality data analysis (e.g., image