
Jonas Kloeckner
Postdoctoral Scholar, Earth and Planetary Sciences
Web page: http://web.stanford.edu/people/jkloeckn
Bio
Jonas Kloeckner is a Postdoctoral Fellow at the Stanford Doerr School of Sustainability, sponsored by the Stanford Institute for Human-Centered Artificial Intelligence (HAI). He specializes in critical mineral exploration essential for the sustainable energy transition. Utilizing his expertise in artificial intelligence and resource forecasting, Mr. Kloeckner leads initiatives that strive to align with global sustainability goals.
Jonas earned his PhD in Engineering from the Federal University of Rio Grande do Sul (UFRGS), Brazil, where he later served as a Postdoctoral Fellow at the Institute of Geosciences. His doctoral and postdoctoral research focused on advancing geostatistical methods for Earth resources forecasting, significantly contributing to the field.
Previously, Jonas was a Visiting Research Scholar at Stanford University under the mentorship of Professor Jef Caers. He holds a Master’s and Bachelor’s degree in Mining Engineering from UFRGS, with additional international studies at Ecole des Mines d’Alès, France, and as a visiting student at Columbia University, USA.
Jonas’s current research integrates spatial data analysis with advanced decision-making processes in subsurface systems, enhancing resource management strategies and supporting sustainable mining practices. Beyond academia, he actively collaborates on various international projects, optimizing resource extraction and minimizing environmental impacts through innovative technology and interdisciplinary collaboration.
All Publications
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Quantifying uncertainty in ultra-deepwater carbonate facies modeling
GEOENERGY SCIENCE AND ENGINEERING
2024; 240
View details for DOI 10.1016/j.geoen.2024.213049
View details for Web of Science ID 001262363300001
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Application of risk assessment to improve sustainability in bauxite mining
RESOURCES POLICY
2021; 74
View details for DOI 10.1016/j.resourpol.2021.102328
View details for Web of Science ID 000700368100091
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Multi-categorical classification using deep learning applied to the diagnosis of gastric cancer
Brazilian Journal of Pathology and Laboratory Medicine
2020; 56: 1-7
View details for DOI 10.5935/1676-2444.20200013
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Use of heterotopic secondary data in geostatistics using covariance tables
APPLIED EARTH SCIENCE-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY
2020; 129 (1): 15-26
View details for DOI 10.1080/25726838.2019.1694326
View details for Web of Science ID 000537544300004
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Covariance table: A fast automatic spatial continuity mapping
COMPUTERS & GEOSCIENCES
2019; 130: 94-104
View details for DOI 10.1016/j.cageo.2019.05.001
View details for Web of Science ID 000478712700009