Applied Physicist

Job Posting Organization:
CERN, the European Organization for Nuclear Research, was established in 1954 and is one of the world's largest and most respected centers for scientific research. With a mission to push the frontiers of science and technology, CERN employs over 2,500 staff members and collaborates with thousands of scientists from around the globe. The organization operates in multiple countries, fostering an environment of innovation and collaboration among physicists, engineers, and other professionals. Diversity is a core value at CERN, and it plays a crucial role in the organization's success and mission to explore the fundamental structure of the universe.

Job Overview:
In this position, you will play a critical role in the development of machine learning-based Particle-Flow reconstruction for the CMS experiment. This involves integrating advanced algorithms into the High Level Trigger as part of the Next Generation Trigger project, where real-time performance and timing constraints are paramount. You will also extend these methodologies to future collider experiments, such as FCC-ee, focusing on optimizing reconstruction performance, evaluating detector-specific strategies, and applying cutting-edge machine learning techniques to enhance physics precision. The role requires a deep understanding of high energy physics and the ability to work with complex data and algorithms to achieve significant advancements in the field.

Duties and Responsibilities:
Your responsibilities will include developing machine learning-based Particle Flow components using TICL inputs in the CMS experiment and validating their performance against standard PF and TICL metrics. You will ensure the robustness, interpretability, and debuggability of these components in realistic CMS environments. Additionally, you will explore the applicability of these approaches for future collider detectors, building on the FCC framework and the Key4hep ecosystem. You will lead machine learning-based reconstruction studies for CLD and extend your approach to other detector concepts such as ALLEGRO, IDEA, and GRAiNITA. Other duties include designing suitable data representations for heterogeneous detector inputs, handling large-scale graphs and distributed training, and benchmarking performance on physics observables and reconstruction metrics.

Required Qualifications:
Candidates must demonstrate proficiency in developing and training machine learning models specifically targeting high energy physics reconstruction, particularly for complex objects like Particle Flow candidates. A deep understanding of high energy physics reconstruction code is essential, showcasing the ability to comprehend, manage, and author reconstruction code tailored for high energy physics experiments. Solid knowledge of detector systems and particle-detector interactions is required for the development of Particle Flow algorithms. Strong programming skills in Python and C++ are necessary, with Python being used for developing and training ML models and C++ for creating efficient and optimized algorithms. Familiarity with CMSSW and FCCSW is a plus.

Educational Background:
Candidates should possess a professional background in Computer Science, Physics, or a related field. A Master's degree with 2 to 6 years of post-graduation professional experience is required, or alternatively, a PhD with no more than 3 years of post-graduation professional experience. It is important that candidates have not previously held a CERN fellow or graduate contract.

Experience:
The position requires a significant level of experience in high energy physics, particularly in the development and training of machine learning models for reconstruction tasks. Candidates should have a proven track record of working on complex projects that involve advanced machine learning techniques and large-scale data processing. Experience in high energy physics experiments and a solid understanding of event reconstruction principles are essential.

Languages:
Fluency in spoken and written English is mandatory, and candidates should be committed to learning French as part of their professional development.

Additional Notes:
The job closing date is set for 05.02.2026 at 23:59 CET. The contract duration is 24 months, with the possibility of extension up to a maximum of 36 months. The position requires a commitment of 40 hours per week and offers hybrid working hours. The target start date for this position is 01-March-202

• The job reference is EP-CMG-DS-2026-8-GRAP, and it falls under the field of Experimental Physics. The benchmark job code is 200140 - Applied Physicist. Compensation includes a monthly stipend ranging from 6372 to 7004 Swiss Francs (tax-free), 30 days of paid leave per year, comprehensive health insurance coverage, family allowances, a relocation package, and opportunities for on-the-job and formal training, including language classes.