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Field
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between INRIA and LJLL which focuses on high performance scientific computing. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7598-PIETOU-006/Candidater.aspx Requirements Research
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the development of scalable software tools and pipelines, potentially leveraging GPU/FPGA accelerators. Our aim is to build next-generation molecular atlases for chronic diseases and to improve patient
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software aspects of large-scale AI systems. Areas of interest may include, but are not limited to: • Advanced accelerator chip technologies, such as GPUs or other specialized chips for large-scale AI
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community. We are committed to excellence, providing unparalleled expertise, and maintaining a world-class standard in service. Please visit us at: https://fs.ucf.edu/ or Facebook and Instagram: UCF
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these resources through a cloud-native Kubernetes environment integrating large-scale CPU and GPU resources, Ceph object storage, BinderHub, Coffea-Casa, Dask, and ServiceX. This platform supports more than 500
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learning, multicore and GPU programming, and highly parallel systems. Good knowledge in one or more of the following programming languages/environments: C/C++, Python, PyTorch (or similar), and Cuda. Place
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-following inverters. Implementing and optimizing scalable algorithms for transient and stability analyses on HPC architectures (CPU, GPU, hybrid). Enhancing the numerical robustness and efficiency of existing
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conducted implementations of algorithms and simulations using contemporary GPU hardware, or Profound knowledge and experience of the DUNE software environment (https://dune-project.org/ ) (knowledge and
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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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construction of a new dataset from Subaru Hyper Suprime-Cam (HSC) and N-body simulations and science projects using those datasets. In addition, the successful candidate will be required to maintain a new GPU