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, with proficiency in Python and deep learning frameworks like PyTorch, Hugging Face, sklearn, tensorflow. Excellent verbal and written communication skills Experience with GPU training and handling large
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precision algorithms for CPUs and GPUs. Performance engineering and analysis including application profiling, benchmarking to identify performance bottlenecks. Verification, and validation of the developed
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. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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. Key attractions are access to a high-performance computing cluster (GPU/CPU and more than 300TB of data), two 3T Prisma MR scanners, and an MR compatible digital EEG system as well as collaboration
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influence the technological trajectory of the ecosystem. The core responsibilities of this position include developing and owning the overall SoC specifications and architecture, encompassing CPU, GPU, memory
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, energy consumption, and accuracy.; ; Training deep learning models, especially in LLMs, faces critical challenges that compromise the optimal use of GPUs. These bottlenecks result in poor computational
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analysis and tuning. Excellent C/C++ and Python programming skills. GPU programming would be welcome (CUDA). Required Documents Resume
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 2 months ago
methods (SFEM) offer superior accuracy per degree of freedom and are naturally suited to HPC architectures (CPU/GPU clusters). Two main Galerkin formulations exist: Continuous Galerkin (CG-SFEM): Memory
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Python programming skills. GPU programming would be welcome (CUDA). Required Documents Resume
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segmentation." CVPR. 2022. [3] van Spengler, Max, and Pascal Mettes. "Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space." ICML. 2025. [4] Pal, Avik, Max van Spengler, Guido Maria D'Amely di