César is a geologist from the University of Chile who, in his thesis, compared machine learning and conventional geostatistical methods. He employed advanced techniques such as neural networks, Support Vector Machines, and Random Forest for copper grade estimation. He is skilled in using StudioRM and Supervisor software for geological modelling, exploratory data analysis, and geostatistical analysis.

Work Experience

During his internship at Datamine Chile, César managed and analysed drill hole databases and learned to use Studio RM and Supervisor software for geological modelling, exploratory data analysis, and geostatistical analysis. He has teaching experience for courses in Stratigraphy and Palaeontology, Sedimentology, and Fundamentals of Crystallography and Mineralogy in the Geology Department at the University of Chile. In the same department, he performed granulometric analysis and processed sediment samples from a fjord, generating stratigraphic columns and mapping sedimentary structures through X-ray imaging of two sediment cores.

Qualifications and Software

César graduated with a geology degree with the highest honours from the University of Chile. He has intermediate knowledge of Studio RM, Supervisor, ArcGIS, QGIS, Python, and SQL and a basic level in ENVI.