Data Reduction Physicist

The NGT Real-time Reconstruction Revolution will transform the CMS High-Level Trigger into an offline-quality reconstruction facility processing up to 750 kHz of events. To make this sustainable, you will lead the design of two complementary data tiers: a low-level reconstructed format that enables trigger rates several times higher than conventional raw-data output while preserving the the option for future reprocessing, and a compact analysis format to save the full 750 kHz stream of online reconstructed events.

Your responsibilities
Design next-generation data structures that push the limits of data reduction while preserving physics performance and analysis flexibility.
Ensure all formats are accelerator-native (e.g., structure-of-arrays, SoA) and optimised for high-throughput GPU processing.
Build an end-to-end framework to rigorously quantify the impact of lossy compression, with clear metrics, reference analyses, and automated regression tests.
Benchmark compression/decompression under realistic workloads: CPU/GPU cost, I/O throughput, memory footprint, and latency.

Your profile
Demonstrated contributions to trigger and/or reconstruction in HEP (or comparable high-throughput scientific software).
Practical understanding of of end-to-end HEP experiment operations, from detector readout to reconstruction, calibrations, datasets, and final physics results.
Experience working in a large international collaboration (code review, CI/CD, documentation) is a plus.
Knowledge with LHC experiments and their data formats is a plus.

Your skills
High proficiency in C++, Python, and ROOT.
Solid understanding of event reconstruction, including calibrations and commonly used data formats in HEP.
Spoken and written English, with a commitment to learn French.

Procedencia : CERN

Convocante : CERN