Im Niklas, a Physics MSc student at ETH Zurich interested in numerical simulation, differentiable models, and scientific machine learning.
My focus is on making high-performance atmospheric models differentiable, enabling gradient-based inference, calibration, and sensitivity analysis in climate and planetary systems.
- Differentiable PDE solvers
- Gradient-based optimization & Bayesian inference
- High-performance scientific computing
- Physics–ML hybrid models
Languages & Tools
Scientific & ML
Dev & Version Control
