about
Jonas GreitemannSenior Software Engineer
I’m a physicist by training and did some research in computational condensed matter physics for my Master’s and PhD programs. During that time, I fell in love with C++ and decided to pursue a career in software engineering.
At my current employer, MVTec Software, I’m part of the team developing MERLIC, an all-in-one solution for industrial machine vision. My focus is on MERLIC’s integration in the automation process on the factory floor. I’ve worked on MERLIC’s compliance with the OPC UA machine vision standard, designed a C API which allows customers to write dedicated communication plug-ins for their particular integration needs, built some plug-ins which are included with MERLIC for common communication protocols such as MQTT, and developed a configuration service based on ZeroMQ to allow remote configuration of both plug-ins and cameras. I’ve also built up some proficiency in UI programming using Qt/QML.
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. I strive to continuously broaden my horizons through attending conferences and meetups, learning and adopting best practices and new language features, always having a toy project on the side to dabble in new tech, and helping out both colleagues and strangers.
experience
Senior Software Engineer at MVTec Software GmbH
since 4/2022
C++ software engineer for MERLIC.
Software Engineer at MVTec Software GmbH
12/2019 – 3/2022
C++ software engineer for MERLIC. Design of a user-facing C API; inter-process communication using ZeroMQ; MQTT; OPC UA; frontend development with Qt/QML.
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.
education
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
Master of Science (Physics) from RWTH Aachen
2015
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
2013
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