| Zusammenfassung: |
The Belle II experiment relies on its Central Drift Chamber (CDC) for precise charged particle tracking and momentum measurement. As the experiment prepares for future upgrades and increased luminosity, improving the CDC track reconstruction algorithms becomes a crucial challenge. The candidate will work within the interdisciplinary CDC-ML team at KIT ETP and ITIV to develop real-time machine learning-based tracking methods.
Special attention will be given to handling background noise and missing hits, as well as optimizing inference on cutting-edge real-time hardware, such as AMD Xilinx Versal AI Edge Series Gen 2 platforms.
The CDC-ML team has access to dedicated hardware at KIT for testing machine learning models on real-time architectures, ensuring that proposed solutions can be validated under experimental conditions. Close collaboration with electrical engineering students at ITIV will be necessary to co-design efficient hardware-software interfaces for real-time inference.
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