Master CSMI : Scientific Computation and Mathematics of Innovation 2025-2026

Advanced instruction in machine learning, Scientific Machine Learning (SciML), optimization, generative models, and model reliability. Provides a solid theoretical core and methodological skills directly applicable to innovation and applied research.

Focus on numerical analysis of PDEs, mathematical modeling, scientific simulation, and optimization (optimal control & numerical optimization). Builds a rigorous foundation for computational engineering and complex industrial applications.

Several course units are structured around supervised projects (academic or external partnerships) fostering project management, integration of computational tools, interdisciplinary collaboration, and translation to real use cases.

Parallel architectures, MPI, OpenMP, GPU programming, hybrid models, performance optimisation and scalability analysis for designing efficient applications on modern platforms.

Data preparation & quality, advanced statistics, supervised / unsupervised learning, pattern mining, interpretability and visual analytics to extract, structure, and communicate relevant information.

Filtering, time–frequency analysis, segmentation, pattern detection, reconstruction, deep learning for signals and images with applications in instrumentation, vision, scientific and biomedical imaging.