Critical mineral and material separations research seeks new ways to glean important elements and other materials from complex and unconventional feedstocks.

Supply chain modeling for critical minerals and materials encompasses a set of analytical, AI, and scenario modeling strategies for mapping, tracking, and simulating risks and potential future outcomes for these supply chains.

Machine learning engineering applies AI techniques to build scalable systems, driving innovation in data processing, automation, and predictive modeling.