Lars Gebraad is an R&D engineer based in Zurich with nine years' experience developing full-waveform simulation and inversion tools for geoscience, nondestructive testing, and structural health monitoring. He builds physics-driven digital twins and scalable Bayesian inference pipelines that turn high-fidelity seismic, guided-wave, and ultrasonic models into practical inspection and monitoring solutions. His PhD work at ETH Zurich combined Hamiltonian Monte Carlo, MCMC, and machine learning on supercomputers to quantify uncertainty in PDE-constrained global seismology problems—skills he now applies to industrial diagnostics and composite inspection at Mondaic. Lars blends deep academic rigor with product-focused engineering, making him adept at delivering simulation-backed answers where conventional methods fail. An unusual strength is his track record of translating large-scale, uncertainty-aware inference into usable tools for both aerospace materials and subsurface imaging.
10 years of coding experience
Master's degree, Applied Geophysics, Cum Laude, Master's degree, Applied Geophysics, Cum Laude at RWTH Aachen University
TU Delft
Middelbare School, Nature & Technology, Middelbare School, Nature & Technology at Vechtdal College Hardenberg
Master's degree, Applied Geophysics, Cum Laude, Master's degree, Applied Geophysics, Cum Laude at Eidgenössische Technische Hochschule Zürich
Academic Minor, Geophysics, Mathematics, Physics, A-, Academic Minor, Geophysics, Mathematics, Physics, A- at The University of British Columbia
Metal Shading Language on Apple M1's GPU for scientific C++.
Contributions:28 commits, 21 pushes, 1 branch in 6 months
apple-m1cppgpuclangcpp17
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