Cms Data Reduction Scientist

**Job Description** Introduction**: As part of our commitment to advancing science, the Next-Generation Trigger (NGT) initiative has launched the Real-time Reconstruction Revolution (R?, "R-cube"). R? 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 R?. The plan is to save data in three different formats: - ** Compact analysis format**: A new, optimized high-level format (e.g., jets, electrons, muons) with very small event sizes (~10 kB) to collect the whole data processed by the HLT farm. It can be used directly for analysis but cannot be processed later with new algorithms and improved calibrations. - ** Raw data format**:A small sample (a few thousand events per second) kept in the standard raw format (~10 MB per event), mainly for key datasets such as high-energy single-muon events. - ** Intermediate format**:A larger sample (tens of thousands of events per second) stored in an intermediate format (hundreds of kB per event) containing low-level reconstructed information. This allows re-reconstruction with improved algorithms or calibrations after data taking. **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. **Your profile**: - Demonstrated contributions to large-scale scientific software or trigger/reconstruction in HEP or similar domains. - Practical understanding of how a large HEP experiment operates, especially the full chain from detector measurements to analysis-level objects and final physics results. - Experience working in large, international collaborations (code review, CI/CD, documentation) is a plus. **Skills**:**Technical Skills**: - Strong proficiency in C++, Python, and ROOT - Solid understanding of event reconstruction, including calibrations, in HEP **Expertise in the following areas will be considered a strong bonus**: - Experience with LHC experiments and their data formats - Data compression techniques - AI/ML methods for data reduction (e.g., autoencoders) - GPU programming - Spoken and written English, with a commitment to learn French. 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. **Additional Information** Job closing date**:06.11.2025** at **23:59 CET**. Contract duration: 24 months, with a possible extension up to 36 months maximum. Working hours: 40 hours per week Job flexibility: Hybrid Target start date: 01-January-2026 Job reference: EP-CMG-2025-179-GRAP Field of work: Applied Physics Benchmark job: 100020 - Research Physicist **What we offer** - A monthly stipend ranging between **6287** and 6911** Swiss Francs per month (net of tax)**. - Coverage by CERN's comprehensive **health scheme** (for yourself, your spouse and children), and membership of the CERN **Pension Fund**. - Depending on your individual circumstances: installation grant; family, child and infant allowances; payment of travel expenses at the beginning and end of contract. - ** 30 days of paid leave per year**. - On-the-job and formal training at CERN as well as in-house language courses for English and/or French. **About us** Begin your CERN Quest. Take Part - Diversity has been an integral part of CERN's mission since its foundation and is an established value of the Organization. Employing a diverse workforce is central to our success._