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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
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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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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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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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: Collaboration: Collaboration and cross-fertilization between labs are strongly encouraged. HPC & AI Supercomputing Resources: An extensive GPU-based accelerated computing facility and high-performance data
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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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of the Empire AI Consortium, researchers have access to state-of-the-art computational infrastructure, including large-scale GPU clusters and high-performance computing resources. The Institute has
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person will focus on either using and/or developing Vlasiator. Prior knowledge in at least one of the following areas is required: GPU technologies, high-performance computing, parallelisation algorithms
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to roll out an advisory track in Computer Engineering, we are seeking qualified experts in Computer Networks, Computer Organization, Data Analytics, Cyber Security and Privacy, Edge/Fog/GPU Computing
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communities, most importantly in helmholtz.ai, the Helmholtz Artificial Intelligence Cooperation Unit: https://www.helmholtz.ai Presenting research results at scientific meetings, conferences, and as scientific