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of advanced language models and derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable
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(OMOP CDM, FHIR) or metadata harmonisation Experience with ETL tools, workflow engines, or bigdata frameworks (e.g., Spark, NiFi, KNIME) Familiarity with containerisation (Docker) and HPC or GPU computing
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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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regulation to neuronal function and circuits. State-of-the-Art Infrastructure: Access to advanced sequencing, imaging platforms, and high-performance GPU computing. Research Environment: An international
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distributed computing for EMT simulations. • Experience with software development in Python, C++, or other programming languages. • Familiarity with GPU acceleration of numerical solvers, parallel sparse
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 2 months 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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-of-the-Art Infrastructure : Access to advanced sequencing, imaging platforms, and high-performance GPU computing. Research Environment : An international, collaborative, and stimulating research setting at a
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 1 month ago
laptop equipped with a GPU and funding to participate in summer schools and national and international conferences. The thesis work will also be supervised by a CNES research engineer as part of
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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