Thesis Title: Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model
This thesis has been published in Ore Geology Reviews (link)
Julian is interested in finding new approaches to tackle future mineral exploration challenges, particularly for critical minerals.
In his research, he links the evolution of subduction zones and downgoing slab properties with the history of porphyry copper (Cu) deposition across the Americas by using a spatio-temporal machine learning approach. By using a wide range of prominent machine learning methods, he provides spatial visualisations in deep-time showing highly prospective areas for porphyry Cu mineralisation along the subduction margins of North and South America.
Julian holds a Bachelor of Engineering (Geological Engineering) from the Universidad Nacional de Colombia. Has 8+ years of experience in mineral exploration mainly for high sulphidation systems in Colombia, Chile, Argentina, and Dominican Republic.