Too good to go: Upcycling Phase-Space Points for Multijet Processes
preprint, 2026
Timo Janßen
I am a junior research group leader at the Institute for Theoretical Physics, University of Göttingen. In my work, I use machine learning and AI to improve the efficiency of first-principles simulations for precision calculations in high-energy physics.
Open PhD position
I am looking for a doctoral researcher to join my junior research group on production-ready AI for unbiased simulation in high-energy physics. The project develops generative models, learned samplers and neural surrogates that speed up event generation and phase-space integration without introducing bias, and makes them available as an open-source library. Four years, fully funded at TV-L 13 (100 %), English working language.
Applications are due 21 September 2026.
Read the job advertisement
Questions and applications: janssen-jobs@physik.uni-obfuscate.goettingen.de
Research interests
Highlighted publications
Selected recent or representative work.
-
-
Monte Carlo event generation with continuous normalizing flows
Phys. Rev. Lett. 137, 091902 (2026)
-
Efficient many-jet event generation with flow matching
Phys. Rev. D 114, 034055 (2026)
-
Accelerating multijet-merged event generation with neural network matrix element surrogates
SciPost Phys. 20, 071 (2026)
-
Sampling NNLO QCD phase space with normalizing flows
JHEP 09 (2025) 194
Contact
Institute for Theoretical Physics
University of Göttingen
Friedrich-Hund-Platz 1
37077 Göttingen
timo.janssen@theorie.physik.uni-goettingen.obfuscate.de