Some of the most well-known examples of the use of machine learning technics in science applications are the detection and classification of gravitational-waves signals from LIGO and Virgo in astrophysics [1], the recent DeepMind Alpha-Fold2 capabilities outperforming classical methods in protein folding [2] or the winning team of the Gordon Bell 2020 with the Deep Potential Molecular Dynamics [3] which is opening new breakthroughs in the drug design process and could speed up future pandemic response efforts. Beyond these key examples, the convergence between HPC and AI is natural where DL-based surrogate modelling is more and more widely applied in research and recent advances in physics-informed neural networks such as HNN [4] bring physical properties and constraints to neural networks loss functions opening a great path towards a new generation of simulation. In Atos, we built a dedicated approach to support the scientific community and Industries by bringing data science and HPC expertise through the Atos Centers of Excellence. Each center is oriented towards a specific domain where our experts and our customers can jointly bring innovations and technologies with the support of some of our partners. Some of the first Atos Centers of Excellence are dedicated to weather forecast & climate changes [5] and life sciences [6].