Bas Peters is an AI research scientist with eight years of experience bridging constrained optimization, inverse problems, and deep learning for real-world science applications. He blends a strong academic background (PhD, UBC) with applied roles across geoscience and bioscience, developing neural-network designs and solvers for structured least-squares problems used in remote sensing and protein modeling. His work emphasizes convex and nonconvex constrained formulations, numerical linear algebra, and reinforcement learning, often recasting vision and imaging tasks as inverse problems to enable principled learning. Currently at Earth Dynamics AI after several research scientist positions, he has a track record of creating new problem formulations and production-ready frameworks that translate geophysical and bioscience data into actionable models. Notably, his profile reflects hands-on experience from USGS and national lab programs to industry deployments, combining domain knowledge with algorithmic rigor.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at The University of British Columbia
Master of Science (MSc), Geophysics, Master of Science (MSc), Geophysics at University of Utrecht
Summer of applied geophysical experience (SAGE), Summer of applied geophysical experience (SAGE) at Los Alamos National Laboratory
Applied Mathematics, Electrical Engineering, Systems and Control, Applied Geophysics, Applied Mathematics, Electrical Engineering, Systems and Control, Applied Geophysics at Delft University of Technology
Contributions:34 pushes, 1 branch in 4 years 9 months
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Bas Peters - AI Research Scientist at Earth Dynamics AI