Jonas GreitemannComputational physicist

I’m passionate about writing clean, scalable code which follows the principle of zero-cost abstraction, combining the readability and safety of a high-level language with the bare-metal efficiency of a systems language. Until recently, I did so as part of my physics doctorate where I quickly built a reputation among colleagues for my advice in all matters C++.

I strive to continuously broaden my horizons, be it by adopting best practices, trying novel language features (C++20), or learning about emerging tech (Rust, WebAssembly) and agile workflows.



Software Architect C++ at MVTec

since 12/2019
MVTec Software GmbH

Research Scientist at LMU Munich

10/2015 – 8/2019

Development of a machine learning framework for the recognition of unconventional magnetic phases in Monte Carlo simulations of frustrated spin systems. This entails the characterization of so-called spin liquids and types of spin order which exhibit multipolar moments rendering them invisible to conventional numeric probes—“hidden order”. The method relies on a combination of support vector machines and spectral graph theory.


Doctoral studies at LMU Munich

until 2019
Chair for theoretical nanophysics; advisor: Prof. Dr. Lode Pollet

Thesis: Investigation of hidden multipolar spin order in frustrated magnets using interpretable machine learning techniques

Springorum Commemorative Coin


Master of Science (Physics) from RWTH Aachen

Institute for theoretical solid state physics; advisor: Prof. Stefan Wessel, PhD

Master's thesis: Quantum Monte Carlo investigation of the one-dimensional Hubbard-Holstein model — Implementation and optimization of a highly parallel Monte Carlo simulation; extensive numerical studies at the Jülich Supercomputing Centre (0.5M CPU-hrs.)

Bachelor of Science (Physics) from RWTH Aachen

Institute for theoretical solid state physics; advisor: Prof. Stefan Wessel, PhD

Bachelor's thesis: Stochastic Analytic Continuation — Implementation of an optimization algorithm (in C++) for solving the inverse problem inherent to the reconstruction of spectral functions from quantum Monte Carlo simulation data

Studienstiftung des dt. Volkes





Städt. Gymnasium Sundern