Helios (High pErformance LearnIng for cOmputational phySics) is a transverse multi-team project at CERFACS. In it, recent developments in the field of Machine Learning are investigated for their potential to revolutionize computational physics, as they have e.g. the field of image processing. Peta-scale databases produced by modern physics simulations have long since outgrown human capacity to analyse them thoroughly, and offer unique opportunities to go beyond historical hand-designed approaches and extend them through systematic data collection and learning. Specifically, Deep Learning, with the training of Artificial Neural Networks, is a very promising technique which is actively investigated for several reasons: its capacity to systematically extract information from previously underexploited databases; its ability to integrate complex multiscale patterns in physical models, to a level of complexity never reached in traditional hand-designed approaches; and for compression, generation and parametrization issues regarding high-dimensional data. Helios is tightly connected to CERFACS' shareholders (Airbus, CNES, EDF, Meteo France, Onera, Safran, TOTAL ) and partners (CNRS, INRIA, University of Erlangen -Germany, ..) with ongoing topics of interest including reservoir modelling, modelling for industrial and environmental risks, satellite data valorisation, weather/climate/environment modelling, combustion and aircraft design.