Data Reduction Physicist

Big Science Sweden; Genf; Vor Ort; CHF 70.000 - 90.000; Vollzeit

Zusammenfassung

Big Science Sweden in Geneva is seeking a candidate to lead the design of next-generation data structures for the CMS High-Level Trigger. Responsibilities include ensuring high-performance data reduction and developing an end-to-end framework to quantify compression impact.

A master's degree with relevant experience or a PhD is required, along with a strong commitment to advancing lossless compression and improving raw data structure.

Qualifikationen

• You have a master's degree with 2 to 6 years of experience or a PhD with max 3 years.
• You are ineligible if you have only a bachelor’s degree.
• No prior CERN fellow or graduate contract.

Aufgaben

• Design next-generation data structures for data reduction.
• Ensure formats are accelerator-native and optimised for GPU processing.
• Build an end-to-end framework to quantify lossy compression impact.
• Benchmark compression under realistic workloads.
• Advance lossless compression using AI/ML techniques.
• Improve raw data structure for higher compressibility.

Ausbildung

Master’s degree with 2 to 6 years of professional experience

PhD with a maximum of 3 years of professional experience

Jobbeschreibung

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.
• Advance lossless compression, leveraging R3-reconstructed objects and pioneering AI/ML techniques.
• Improve raw data structure to achieve higher compressibility and faster data access.

Be sure to meet the eligibility criteria

• By the application deadline, you have a master’s degree with 2 to 6 years of professional experience since graduation or a PhD with a maximum of 3 years of professional experience since graduation. You are not eligible with only a bachelor’s degree.
• You have never had a CERN fellow or graduate contract before.
• Please pay attention to the additional criteria and requirements for this specific position and mentioned above.