CMS Data Reduction Scientist (EP-CMG-2025-179-GRAP) at CERN

Geneva, Geneva, Switzerland -

Full Time

Start Date

Immediate

Expiry Date

14 Jan, 26

Salary

6911.0

Posted On

16 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

C++, Python, ROOT, Event Reconstruction, Data Compression, AI, Machine Learning, GPU Programming, Physics, Applied Physics, Data Science, High-Energy Physics, International Collaboration, CI/CD, Documentation

Industry

Research Services

Description

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 At CERN, the European Organization for Nuclear Research, physicists and engineers are probing the fundamental structure of the universe. Using the world's largest and most complex scientific instruments, they study the basic constituents of matter - fundamental particles that are made to collide together at close to the speed of light. The process gives physicists clues about how particles interact, and provides insights into the fundamental laws of nature. Find out more on http://home.cern. We are on a Quest. A Journey into discovery like no other. Bring your expertise to our unique work and develop your knowledge and skills at pace. Join world-class subject matter experts on unique projects, in a Quest for greater knowledge and deeper understanding. 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.

Responsibilities

Define next-generation data formats for data reduction while preserving physics performance. Develop workflows to quantify the impact of lossy compression on physics performance and benchmark compression methods.