Applied Physicist for Particle-Flow Reconstruction (EP-CMG-DS-2026-8-GRAP) at CERN

Geneva, Geneva, Switzerland -

Full Time

Start Date

Immediate

Expiry Date

09 Apr, 26

Salary

6911.0

Posted On

09 Jan, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Machine Learning, High Energy Physics, Python, C++, Data Representation, Distributed Training, Event Reconstruction, Detector Systems, Physics Analysis, ML Model Creation, Robustness, Interpretability, Debuggability, Performance Benchmarking, Collaboration, Algorithm Development

Industry

Research Services

Description

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: 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! At CERN, the European Organization for Nuclear Research, we are pushing the frontiers of science and technology. Our groundbreaking work brings together not only physicists but also a diverse range of professionals from engineering, technical, scientific, and administrative fields. Together, we foster an environment where innovation and collaboration thrive. Every day, we face exciting new challenges and opportunities to contribute to cutting-edge research that shapes our understanding of the universe. We meet these challenges through the diverse perspectives within our teams, ensuring every contribution is valued and driving our shared sense of inclusion and purpose. Diversity is a core value of CERN since its foundation, and it remains central to our mission and continued success. If you are ready to be part of a dynamic, inclusive community pushing the boundaries of knowledge, CERN is the place where your curiosity and skills can thrive. Be part of our mission to uncover what lies at the heart of the universe! TAKE PART! More information about us, here: careers.cern

Responsibilities

You will develop machine learning-based Particle-Flow reconstruction for the CMS experiment and integrate advanced algorithms into the High Level Trigger. Additionally, you will extend these approaches to future collider experiments, optimizing reconstruction performance and applying cutting-edge ML techniques.