As 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.

Your responsibilities:

• Defining 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).
• Developing a workflow to quantify the impact of lossy compression on physics performance, including clear metrics, reference analyses, and automated regression tests.
• Benchmarking compression/decompression methods by measuring CPU/GPU cost, I/O throughput, memory footprint, and latency under realistic workloads.
• Improving 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.

Eligibility criteria:

• You are a national of a CERN Member or Associate Member State.
• You have a professional background in Physics, Applied Physics, Data Science (or a related field) and have either:
  • a Master's degree with 2 to 6 years of post-graduation professional experience;
  • or a PhD with no more than 3 years of post-graduation professional experience.
• You have never had a CERN fellow or graduate contract before.