Job Description
In this role, you will contribute to the development of machine learning-based Particle-Flow reconstruction for the CMS experiment, integrating advanced algorithms into the High Level Trigger as part of the Next Generation Trigger project, where timing constraints and real-time performance are critical. You will further extend these approaches to future collider experiments such as FCC-ee, including optimising reconstruction performance, evaluating detector-specific strategies, and applying cutting-edge ML techniques to improve physics precision.
Your responsibilities
Develop ML-based PF components using TICL inputs in CMS and validate performance using standard PF and TICL metrics.Ensure robustness, interpretability and debuggability in realistic CMS environments.Explore the applicability of these approaches for future collider detectors, building on the FCC framework and the Key4hep ecosystem.Lead ML-based reconstruction studies for CLD, and extend the approach to other detector concepts such as ALLEGRO, IDEA, and GRAiNITA.Designing suitable data representations for heterogeneous detector inputs.Handling large-scale graphs and distributed training.Benchmarking performance on physics observables and reconstruction metrics.Your profile
Proficiency in developing and training ML models targeting HEP reconstruction, ideally of complex objects like Particle Flow candidates.Deep understanding of High Energy Physics (HEP) Reconstruction Code, showcasing proficiency in comprehending, managing, and authoring reconstruction code tailored for High Energy Physics experiments.Solid knowledge of detector systems and particle-detector interactions, as required for Particle Flow algorithms.A strong foundation in programming is essential, with a focus on python for developing and training ML models and C++ for the development of efficient and optimised algorithms.Skills
Demonstrated proficiency in detector physics, event reconstruction principles and physics analysis in the context of High Energy Physics experiments is essential.Strong experience in advanced ML model creation, large scale and distributed training, and deployment is required, as the role involves developing and incorporating AI-driven techniques into the reconstruction algorithms.Strong programming skills in Python are necessary for scripting, tooling, and integration tasks.Strong programming skills in C++ are required, with a focus on developing efficient algorithms and, eventually, integrating different ML into HEP framework for fast inference; familiarity with CMSSW and FCCSW is a plus.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 Computer Science, Physics, or related (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: 29.01.2026 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-March-2026
Job reference: EP-CMG-DS-2026-8-GRAP
Field of work: Experimental Physics
Benchmark job: 200140 - Applied Physicist
Global Benefits
A monthly stipend between 6287-6911 Swiss Francs per month (tax free) depending on your degree.30 days of paid leave per year plus 2 weeks annual closure.Coverage by CERN’s comprehensive health insurance scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund.Family, child and infant monthly allowances depending on your individual circumstances.A relocation package (installation grant and travel expenses) depending on your individual circumstances.Possibility to extend your contract up to 36 months.On-the-job and formal training including language classes.Overview of CERN - Discover a world where the impossible is made possible!
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