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multiphase flows Your tasks Develop and extend the in-house GPU-accelerated multiphase Lattice Boltzmann (LBM) code for DNS-grade boiling multiphase flow related to nuclear reactor operation, including bubble
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 25 days 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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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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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 14 days ago
operates the first Czech quantum computer named VLQ. https://www.it4i.cz/ Activity description: · research and development of methods for acceleration of parallel applications in the High Performance
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inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native
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degree in physics, mathematics or any related field; correspondingly, Postdocs hold a PhD or equivalent degree in the above mentioned fields. What we offer State of the art on-site high performance/GPU
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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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, including experience with large-scale GPU or cluster-based training, reproducible experiment pipelines, versioned datasets, and systematic evaluation. Ability to formulate research questions, run empirical
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(static and dynamic photochemistry, heterogeneous catalysis, modeling of interfaces and ionic liquids). It benefits from access to the CBPSMN mesocenter, with a large amount CPUs and GPUs facilities. In
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-of-the-art compute and GPU infrastructure, including H100 and B300 GPU clusters. Innovation: The opportunity to apply a recently published, "proof-of-concept" method for synthetic enhancer design to a critical