The successful candidate will lead new machine learning developments to improve several aspects of the existing tracking software, with a special emphasis on seeding and the reconstruction of anomalous tracks, while accounting for time-dependent detector conditions. The code developments will be done within the ACTS framework, an experiment-independent toolkit for charged particles track reconstruction. The most promising developments will later be imported into the ATLAS experiment software, and validated on real data, collected during Run 3.
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