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. A limited number of positions are also available to candidates from Non-Member States.
• 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: 05.02.2026 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 6372-7004 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.

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