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algorithms. The post-doc will carry out theoretical work on causal abstraction and causal alignment, implement algorithms and experimental pipelines in Python/PyTorch, and run experiments on GPU clusters
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time steps, will be explored. For these two projects, the GPU-based version of SCHISM under development will also be tested. Type of recruitment 12-month postdoctoral contract based in La Rochelle (17
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-atmosphere dynamics. We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will
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frameworks, e.g., Caffe, TensorFlow, PyTorch, and GPU-acceleration frameworks, e.g., CUDA will be a plus. Outstanding SW development and programming skills in C++, Python, ROS tools and libraries. Excellent
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-atmosphere dynamics. We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will
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programming in C++ and Python. - Mandatory mastery of GPU programming (CUDA) for optimization. - Experience with Deep Learning frameworks (PyTorch). - Knowledge of the AliceVision architecture is a major asset
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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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. 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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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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(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