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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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/Qualifications Experiencia en programación e investigación en rendering (simulación óptica): -Modelos de simulación de pelo (Marschner, Yuksel, etc) -Modelos de render inverso mediantepath tracing (Mitsuba) y
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
Bayesian statistics, AI-assisted inverse problems, planetary remote sensing, and environmental monitoring. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-09787 Requirements Skills
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HisModSim project obtained by Natacha Gillet at the Laboratoire de Chimie of the ENS de Lyon. It focuses on the impact of post-translational modifications or histone variant modifications in the nucleosome
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2026, UTC will deploy a state-of-the-art scientific computing and storage infrastructure, comprising a high-memory, GPU-enabled HPC computing node and a modular, scalable RAID-based storage system
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optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 2 months ago
and domain experts on highly scalable parallel applications with focus on: - development and implementation of parallel aplications, - GPU acceleration of applications, - application optimization
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approaches, the application of meta learning, and the integration of convex optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms
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. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP), Horovod, or DeepSpeed, and in
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University is home to Türkiye’s largest GPU cluster, providing advanced infrastructure for leading-edge AI research. The Department of Computer Science and Engineering at Koç University has world-renowned