Engineering Science (B.Sc.) at the Technical University of Munich, finishing 2026. I work at the intersection of numerical methods, probabilistic inference, and computational finance. My current focus is Bayesian inverse problems: dimensionality reduction via Likelihood-Informed Subspaces and Gaussian-process surrogate modeling, applied to local-volatility calibration.
Looking for quantitative research / quantitative engineering roles and a Master's in Applied Mathematics or Mathematical Finance.
- Bachelor's thesis — Bayesian Inverse Problems with Dimensionality Reduction Engineering Risk Analysis Group, TU Munich. Likelihood-Informed Subspace (Spantini et al., 2015) combined with Gaussian-process surrogates, validated on Black-Scholes local-volatility calibration: recovering a latent volatility field from noisy option prices through a linearized parabolic-PDE forward operator. → LIS-Bayesian-Inverse-Problems
Numerical PDEs (finite differences / finite elements), Bayesian inference and uncertainty quantification, surrogate modeling, optimization.
Languages & tools: Python (NumPy / SciPy), MATLAB, R, SQL, C, Git, LaTeX
Spoken: Spanish (native) · German (C1) · English (C1)
erickmuroz@gmail.com · Munich, Germany