CMS Data Reduction Scientist

ppAs part of our commitment to advancing science, the Next-Generation Trigger (NGT ) initiative has launched the Real-time Reconstruction Revolution (R³, "R-cube"). R-cube aims to transform the High-Level Trigger (HLT) farm into a full reconstruction farm capable of processing all input data (up to 750 kHz, ~6 TB/s) with offline-like quality through improved reconstruction code and calibrations. A major challenge is managing the storage of the high-quality data produced by R³. /p h3Your responsibilities: /h3 ul liDefining next-generation data formats that maximize data reduction while preserving physics performance. Ensure they are parallel-programming friendly (e.g., structure-of-arrays, SoA) and optimized for accelerators (e.g., GPUs). /li liDeveloping a workflow to quantify the impact of lossy compression on physics performance, including clear metrics, reference analyses, and automated regression tests. /li liBenchmarking compression/decompression methods by measuring CPU/GPU cost, I/O throughput, memory footprint, and latency under realistic workloads. /li liImproving lossless compression of raw data by tuning existing methods and designing new ones that leverage R?-reconstructed objects and AI techniques; where feasible, refactoring raw data structures to enhance compressibility and I/O efficiency. /li liYou have a professional background in Physics, Applied Physics, Data Science (or a related field) and have either: ul liMaster's degree with 2 to 6 years of post‑graduation professional experience; /li liPhD with no more than 3 years of post‑graduation professional experience. /li /ul /li liYou have never had a CERN fellow or graduate contract before. /li /ul /p #J-18808-Ljbffr