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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go
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Science Beamline (P61A/WINE), the lmaging Beamline (P05/IBL), and the Nanofocus Endstation of Micro? and Nanofocus X-ray Scattering Beamline (P03/MINAXS), along with supporting laboratories (https
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of central London. For more information: https://www.kcl.ac.uk/engineering About the role This role will support the delivery of a mesh generation project, funded under a recent major £7m EPSRC Programme Grant
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of central London. For more information: https://www.kcl.ac.uk/engineering About the role This role will support the delivery of a mesh generation project, funded under a recent major £7m EPSRC Programme Grant
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Endstation of Micro‑ and Nanofocus X-ray Scattering Beamline (P03/MINAXS), along with supporting laboratories (https://gems.hereon.de). The techniques offered at our diffraction beamline P61A have a strong
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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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development. To validate your developments, you will be provided with access to the top European supercomputers (Adastra, Jean-Zay, etc.). Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UAR3441
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to clinical data; design interpretable representation spaces for tumor-immune dynamics and therapy response. Technical Expertise (25%) – Engineer LLM and diffusion/flow-matching pipelines; integrate multi-GPU
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will benefit from the Jean Zay supercomputer to perform intensive GPU calculations but also MARBEC/LIRMM GPU shared resources (https://umr-marbec.fr/recherche/dispositifs-de-recherche/den/ ). His/her
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models, LLMs and Transformer architectures Excellent programming skills in PyTorch/JAX and experience working with GPUs and high-performance clusters. Strong mathematical skills with excellent