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that combine parallel architectures (i.e., GPUs or accelerator boards, clusters) and numerical algorithms suited to such architectures with the goal of improving the speed of convergence and the stability
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., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with team members
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theoretical physics. prior work in many-particle quantum mechanics. excellent knowledge and previous experience in HPC and proven competence in c++ and GPU computing. good ability to work independently and to
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). Resources: Access to a project-dedicated H200 GPU cluster, travel funding for top-tier conferences, and the broader compute resources of the Vector Institute/Digital Research Alliance of Canada. How to Apply
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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at the interface of AI, imaging, microbiome research, and laboratory automation within a Helmholtz-wide collaboration. Access to state-of-the-art live-cell imaging, microfluidics, laboratory automation, and GPU
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atmospheric modeling, artificial intelligence, and near real-time applications. The successful candidates will contribute to the development of high-performance and GPU-enabled modeling tools for wildfire
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for multi-core processors, with particular emphasis on the CUDA platform, which enables the use of graphics processing units (GPUs) for general-purpose tasks, history of application for external financing
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
, expertise, and powerful computational resources (GPUs & CPUs) of the team. This PhD position is fully funded within the framework of RoGSiLT: Robust and Generalizable Sign Language Translation , a
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infrastructure, model training, and inference systems. You'll design, develop, and optimize scalable data pipelines and build multi-node GPU training and inference pipelines for foundational models. You'll also