FWF Elise Richter Grant V1000

Computational Uncertainty Quantification in Nanotechnology          PI: Leila Taghizadeh


The goals of this project include

1) to investigate how the uncertainty propagates through the mathematical models in nanoelectronical devices and effects the output (stochastic modeling),

2) to estimate the unknown parameters in the model using statistical Bayesian inversion and probabilistic methods (model calibration), and

3) optimal experimental design in nanotechnology.

Topics for Bachelor's and Master's Theses

  • MCMC methods for high-dimensional Bayesian inverse problems
  • Sequential Bayesian optimal experimental design
  • Neural network surrogate methods for Bayesian inverse problems
  • Multilevel methods for Bayesian optimal experimental design

       Interested? Simply get in touch with me!


Supervised Students

  • Manuel Gehmeyr (Co-supervised at TU Munich, 2022)

    Master Thesis: Bayesian Reconstruction and Optimal Experimental Design for Multispectral Optoacoustic Tomography.

  • Beatrix Rahnsch (Co-supervised at TU Munich, 2023)

    Bachelor Thesis: Network-based Inference for the Prediction of the COVID-19 Spread.