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the AliceVision library. Activity 3: Critical Code Optimization (C++/CUDA) - Adapt the code to drastically reduce computation times (target: < 1h). - Replace proprietary dependencies (InstantNGP) with a flexible
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Proven experience with multiple parallel programming paradigms, including but not limited to; MPI, OpenMP, and CUDA (Compute Unified Device Architecture) Experience in a batch HPC environment with a
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Sensing) Programming skills: Python, CUDA, C++ Strong motivation and commitment to research, teaching and administration The successful candidate will be expected to work effectively in a team environment
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/C++, Fortran, and/or Python Proven experience with multiple parallel programming paradigms, including but not limited to; MPI, OpenMP, and CUDA (Compute Unified Device Architecture) Experience in a
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, tensor train methods, and/or C++/CUDA programming and a solid foundation in code development is especially welcome. LanguagesENGLISHLevelExcellent Research FieldPhysicsYears of Research Experience1 - 4
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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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processing modules, notably with the PyNX software (http://ftp.esrf.fr/pub/scisoft/PyNX/ ); Support ESRF beamlines exploiting coherent X-ray imaging techniques; Conduct an original research project exploiting
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polarimetry for the metrology in microelectronics, studies of nanomaterials, and optical diagnosis of various biological tissue including plants. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre
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learning, multicore and GPU programming, and highly parallel systems. Good knowledge in one or more of the following programming languages/environments: C/C++, Python, PyTorch (or similar), and Cuda. Place
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software applications to support study aims using MATLAB, C++, Python, CUDA, ROS, JAVA, MS .net, Arduino, or other languages as needed. (20%) Support IRB and R&D applications. (15%) Support grant and