Development of a toolchain for automated classification of track segments for the Belle II experiment.

Environment:

ITIV is working as a project partner of the Belle II experiment in Tsukuba, Japan on the next generation of trigger systems for particle accelerators as part of a detector upgrade to gradually increase the luminosity of the experiment. In the process, we are exploring how the FPGA-based trigger system can be better adapted for increasing data volumes.

Task:

With the advent of machine learning algorithms in latency-optimized trigger systems, the demand on the configurability of the overall system increases. As part of a bachelor thesis, a concept is to be designed on how regular firmware updates can be generated automatically based on a database.

Requirements:

• Interest in software development, machine learning or automation.
• Python, C++ are advantageous