Fabian Joswig
About Fabian Joswig
Fabian Joswig is a Research Scientist at DeepL, specializing in scaling AI models. He has a strong academic background in physics, holding a PhD in Theoretical and Computational Particle Physics and experience in High Performance Computing.
Current Role at DeepL
Fabian Joswig currently serves as a Research Scientist at DeepL, a position he has held since 2023. In this role, he focuses on scaling AI models, leveraging his expertise in high-performance computing. His work contributes to the development of advanced language processing technologies at DeepL, enhancing the efficiency and effectiveness of AI applications.
Previous Experience at The University of Edinburgh
Before joining DeepL, Fabian Joswig worked as a Postdoctoral Research Associate at The University of Edinburgh from 2021 to 2023. During his tenure, he conducted research on numerical simulations of Quantum Chromodynamics at the Higgs Centre for Theoretical Physics. This experience allowed him to deepen his understanding of theoretical physics and computational methods.
Educational Background in Physics
Fabian Joswig has a comprehensive educational background in physics. He earned his Bachelor of Science (B.Sc.) from the University of Münster, followed by a Master of Science (M.Sc.) in Physics from the same institution. He further advanced his studies by obtaining a Doctorate (Dr. rer. nat.) in Theoretical and Computational Particle Physics from the University of Münster, completing his PhD in 2021.
Research Experience at University of Münster
At the University of Münster, Fabian Joswig held multiple research positions. He worked as a PhD Candidate and Research Assistant from 2017 to 2021, where he contributed to various projects in theoretical physics. Additionally, he briefly served as a Postdoctoral Researcher in 2021 for four months, furthering his research in the field.
High Performance Computing Expertise
Fabian Joswig has a strong background in high-performance computing, which he applied extensively in his research prior to joining DeepL. His expertise in this area has been instrumental in his work on scaling AI models, allowing for efficient processing and analysis of complex data sets in his current role.