Resume

SUMMARY 

• Ph.D. in Computational Mathematics: Optimisation, uncertainty quantification, numerical differential equations. International education and internship experience.
• Implemented/contributed to 5+ scientific programming projects in Python, Matlab, and C++.
• Wrote two highly innovative, long-form scientific papers for high-impact journals.
• Presented at 11+ conferences and taught 7 courses (tutoring and writing examples, most in German).

PROFESSIONAL EXPERIENCE

Software developer for geolocation algorithms and GNSS data processing

RIEGL Laser Measurement Systems Vienna, AT. Feb 2026 – Present

• Implemented a method to correct biased GNSS data from urban canyons
• Designing and implementing new real-time kinematics algorithms to dramatically increase the baseline for reference stations

Postdoc Researcher in Computational Mathematics

University of Vienna, AT. Oct 2024 – Sep 2025

• Reduced order modelling (reduced basis method) of nonlinear parametric PDEs (in review). 
• Deep neural networks and space-time finite elements for the approximation of the stochastic wave equation (in progress).

University Assistant in Computational Mathematics

TU Wien Vienna, AT. Nov 2019 – Oct 2024

• Researched approximation of challenging, nonlinear stochastic PDEs with sparse grid-finite element algorithms. Reported to the professor, 3 international collaborators.
• Designed and implemented SGMethods: High-dimensional Sparse Grid Interpolation (Python, see GitHub). Tested on nonlinear parametric PDEs with 100+ scalar unbounded parameters.
• Designed and implemented (Matlab) an adaptive sparse grid-finite element algorithm. Reduces cost by ~100x compared to uniform meshes. Fully automatic, no hyperparameter selection.
• Secured €3500 funding (Christiane Hörbiger Preis) used for a research trip to Australia.
• Organised and coordinated events as 1 of 4 student speakers of the Vienna School of Mathematics.

Optimization Algorithms & Programming Internship

Fluxim AG Winterthur, CH. Feb – Aug 2018

Fluxim AG develops world-renowned simulation software and measurement instruments for semiconductor devices (solar cells and OLEDs). Customers: Stanford University, ETH Zurich, Csiro, …

• Reported to technical consultant to research and implement global optimisation algorithms in company’s software. Independent interaction with technical staff to find relevant test cases.
• Researched and tested (Python) ~10 algorithms on challenging parametric solar cell setups.
• Collected the best 3 algorithms in a C++ library for integration in the company’s software (C++). 
• Reduced simulation time by a factor of ~10 and increased possible accuracy.

EDUCATION

Ph.D. Computational Mathematics

TU Wien Vienna, AT. Nov. 2019 – Oct. 2024

Grade: Sehr Gut mit Auszeichnung. See the “University Assistant” position above.

M.Sc. Computational Science and Engineering

EPFL Lausanne, CH. 2016 – 2019. GPA 5.37/6. 

EPFL ranks among the 15 best universities worldwide in QS, THE, and ARWU rankings. The CSE programme is restricted to ~30 students per year, and admission is highly competitive. 

• Designed and implemented (extended Matlab GeoPDEs library) Trimmed Isogeometric Analysis of Stokes Problem in Master Thesis, Post-Master Internship (Sep. 2018 – Aug. 2019). 
• Researched cardiovascular modelling (C++ finite elements, Paraview data visualisation) and stochastic simulation (C++ finite elements, original theoretical analysis) as semester projects.
• Assisted with the teaching of the Numerical Optimisation course and the scientific research in cardiovascular modelling.

B.Sc. Mathematics

Università degli Studi di Trento (IT). 2013 – 2016. 110/110 cum Laude 

The undergraduate sciences programmes rank 1st in the CENSIS national universities ranking 2024.

TECHNICAL SKILLS SUMMARY

Programming: Python, Matlab (advanced), C, C++ (intermediate)

Tools: Git, Sphinx, Pytest, VS Code (frequent use)

Technologies: OpenMP, MPI, CUDA (basic)

LANGUAGE SKILLS

English advanced

German intermediate

Italian mother tongue

French basic