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- l'institut du thorax, INSERM, CNRS, Nantes Université
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such as NumPy, SciPy, PyTorch or TensorFlow Experience with C/C++ and GPU/accelerator platforms is an asset Hands‑on experience with software‑defined radio platforms, RF measurement equipment, or laboratory
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frameworks like PyTorch, Hugging Face, sklearn, tensorflow. Excellent verbal and written communication skills Experience with GPU training and handling large medical datasets e.g., large magnetic resonance
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. Experience with high-speed data acquisition, signal processing, or FPGA/GPU-based DSP is considered an advantage. The ability to work independently while contributing effectively to a collaborative research
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. Knowledge and Professional Experience: DFT-based methods. Scientific programming in Fortran, in MPI/OpenMP-parallelised codes. Knowledge of other languages (in particular python) and of GPU offloading will be
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(Xilinx Vitis/Vivado, Intel Quartus, HLS tools) HPC environments or GPU-accelerated computing On-detector firmware or data acquisition systems Familiarity with HEP data formats and reconstruction
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simulation frameworks or HPC/GPU-accelerated ML. Proficiency in scientific software development (Python/ML stack, MATLAB for wireless simulation, reproducible workflows, version control). Strong publication
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provide competitive compensation packages and full support for conference travel and professional development. You'll have access to state-of-the-art high-performance computing infrastructure and GPU
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disease insights. The lab has state-of-the-art computing capabilities with an in-house cluster serving 80 CPU cores and 1.5TB of RAM, as well as a newly acquired NVIDIA DGX box with eight H100 GPUs and 224
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l'institut du thorax, INSERM, CNRS, Nantes Université | Nantes, Pays de la Loire | France | 2 months ago
Devices" ). • Bring various improvements on the synthetic model (vasculature shape / aneurysm / background noise modelling) • Numerical simulations will be performed on a GPU HPC cluster. • Programming in
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exposing hardware accelerators, such as GPUs and FPGAs, in a seamless and portable way. This includes designing execution logic and resource-scheduling strategies that make efficient use of available