Postdoctorate in Machine Learning for Predictive Modeling of Mineral Fragmentation Behavior

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Context and contributions of the position . As part of the PEPR Sous-sol, bien commun program, the InnovTech project federates eight French research institutions around the sustainable processing of mineral resources, with an explicit focus on artificial intelligence and machine learning. This postdoctoral position is part of Sub-Task ST3.1.1: Closing the gap between elementary ore fragmentation properties and the performances of comminution technologies (Leader: INP/LGC - Participants: BRGM, LGF/MSE). Comminution accounts for approximately 50% of the energy consumption of world mineral processing plants. ST3.1.1 proposes a bottom-up methodology: starting from particle-scale fragmentation data generated by dedicated experimental tools and DEM simulations, coupled machine learning and phenomenological models will bridge elementary fragmentation properties and the performance of comminution machines. The ML problem is the following. Three complementary data sources characterize each mineral particle: 3D micro-CT volumes (crack networks, porosity, mineralogy), single-particle breakage test results (fracture energy, breakage probability, fragment size distributions), and DEM simulation outputs (fracture mechanisms, energy dissipation). Job description . The objective is to learn a compact and transferable representation of particle breakage behavior, what we call the fragmentability sig