You will work on the development of simulation-based inference (SBI) methods, in particular
diffusion models, and their application to the analysis of data from experiments at the Large
Hadron Collider (LHC). Your area of responsibility includes the following activities:
● Implementation of SBI methods
● Scaling SBI methods to distributed computer systems
● Extension of SBI methods with regard to their interpretability
● Application of SBI methods to the analysis of LHC data
This work is part of the project Neural-based Diffusion Likelihood-free Estimations
(NEEDLE). This joint project of KIT and DESY is funded by Helmholtz AI. The task offers the
possibility of a doctorate in physics.
● Master's degree in physics or computer science
● Very good knowledge of elementary particle physics
● Very good programming skills
● Experience with statistical data analysis and machine learning
● Very good written and spoken English skills.
Salary category 13, depending on the fulfillment of professional and personal requirements.
3 years
31.07.2024
For further information, please contact Prof. Dr. Ulrich Husemann, ulrich.husemann@kit.edu, phone +49 721 608-24038.