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efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow prediction Integration of domain decomposition methods into the learning framework to enable
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the supervision of one or more of its members, in one of the following projects: - Fundamental physics: the ESPRESSO road to ANDES - Dark Energy, From Alpha to Omega - Coding the Cosmos in the GPU Era: Do
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 1 month ago
deterministic inversion approaches. Low-order arithmetic offers promises of important cost-reduction via the use of GPUs, and is commonly used in learning approaches, it has therefore become a central block of an
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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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samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize, and validate a refractive
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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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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 29 days 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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publications at top-tier venues such as CVPR, ICCV, ECCV, NeurIPS or ICRA. You will have access to extensive compute resources at TU Delft, ranging from local GPU servers to large-scale HPC infrastructure
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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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-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