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, convolutional architectures and surrogate modelling for physical systems Solid understanding of PDE-based models or the motivation to acquire this knowledge Experience with real-time or edge deployment (CUDA
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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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Information Eligibility criteria - PhD in astronomy, computer science or related fields - Proficiency with several of the following languages / programming models: C/C++, Python, CUDA, OpenMP, MPI, PyTorch
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, CUDA or equivalent), is expected. The demonstrated ability to tackle multiscale problems and to contribute to the development of innovative imaging prototypes is highly desirable. 3) Familiarity with