Summary
Niklas Götz is a computational physicist-turned-software engineer with 10 years of experience building high-performance simulation and data pipelines for scientific and industrial problems. He recently completed a PhD in Theoretical and Computational Nuclear Physics, combining large-scale HPC simulations of heavy-ion collisions with Bayesian inference, statistical modeling, and machine learning to extract transport coefficients under extreme conditions. Equally comfortable in Python and C++, he has a proven track record of shipping robust, well-tested scientific software and automated quality-control systems that are widely used in his field. His background includes introducing generative ML methods into high-energy physics and packaging them for non-experts, reflecting a talent for turning advanced research into practical tools. Fluent in French and experienced mentoring students across interdisciplinary teams, he thrives in multicultural, research-driven environments. Currently at Black Forest Labs, he focuses on large-model data problems that bridge physics-informed modeling and scalable ML engineering.
10 years of coding experience
Doctor of Philosophy - PhD Theoretical Nuclear/Particle Physics, Doctor of Philosophy - PhD Theoretical Nuclear/Particle Physics at Frankfurt Institute for Advanced Studies
High School Diploma General Secondary Education and A-Levels, High School Diploma General Secondary Education and A-Levels at Jack-Steinberger-Gymnasium Bad Kissingen
French language and literature CEFR C1, French language and literature CEFR C1 at Eurocentres
Master of Science - MS High Energy Physics, Master of Science - MS High Energy Physics at École Polytechnique
Bachelor of Science - BS Mathematical Physics, Bachelor of Science - BS Mathematical Physics at The Julius Maximilians University of Würzburg
Master of Science - MS High Energy Physics, Master of Science - MS High Energy Physics at ETH Zürich
English for Engineering, English for Engineering at Eurocentres Language Schools
German, English, French, Latin, Spanish, Hindi