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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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decays and related physics object performance studies , development of the real-time analysis (RTA) in particular with ML/AI reconstruction on hybrid GPU/FPGA architecture for the electromagnetic
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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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- Knowledge and experience with Python, Fortran and/or GPU computing - HPC and parallel libraries such as OpenMP and MPI - HPC parallel IO libraries such as HDF5 or NetCDF - Experience with supercomputer tools
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typically work with datasets of up to several TBs depending on the case study); - Autonomy to conduct independent analysis and research on our (GPU/CPU) servers, familiarity with coding frames in machine
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-dependent approaches. -Proficiency in high-performance computing (MPI/OpenMP/GPU) and scientific code development is a plus. -Interest in attoscience and/or matter–antimatter physics is an asset. -Ability
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secretion systems (T4SS). The laboratory provides all the equipment required for the project, including standard microbiology facilities (L1 and L2) and biochemistry equipment (AKTA pure), GPU computing