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 over 2,500 employees and thousands of scientists from around the globe collaborating on various projects, CERN operates in multiple countries, primarily in Europe. The organization is dedicated to pushing the boundaries of science and technology, particularly in the field of particle physics, and is known for its groundbreaking work in understanding the fundamental components of matter and the universe.

Job Overview:
In this role, you will be instrumental 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 timing constraints and real-time performance are critical. You will also extend these approaches 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 position requires a deep understanding of high energy physics and the ability to work with complex data and algorithms to improve the efficiency and accuracy of particle detection and analysis.

Duties and Responsibilities:
Your responsibilities will include developing ML-based Particle Flow components using TICL inputs in CMS and validating their performance using standard PF and TICL metrics. You will ensure robustness, interpretability, and debuggability 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 ML-based reconstruction studies for CLD and extend the 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 targeting high energy physics reconstruction, particularly for complex objects like Particle Flow candidates. A deep understanding of High Energy Physics (HEP) Reconstruction Code is essential, showcasing the ability to comprehend, manage, and author reconstruction code tailored for HEP experiments. Solid knowledge of detector systems and particle-detector interactions is required for developing Particle Flow algorithms. Strong programming skills in Python and C++ are necessary, with Python used for developing and training ML models and C++ for creating efficient algorithms. Familiarity with CMSSW and FCCSW is a plus.

Educational Background:
Candidates should have 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 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 candidates to have a significant level of experience in high energy physics and machine learning, particularly in the context of developing and deploying advanced ML models. Experience in large-scale and distributed training is essential, as the role involves integrating AI-driven techniques into reconstruction algorithms. Candidates should also have a strong foundation in programming and algorithm development, particularly in Python and C++.

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 contract duration is 24 months, with a possible extension of up to 36 months. The working hours are set at 40 hours per week, and the position offers hybrid job flexibility. The target start date for this role is March 1, 202

• The job reference is EP-CMG-DS-2026-8-GRAP, and it falls under the field of Experimental Physics. The position offers a monthly stipend between 6287-6911 Swiss Francs (tax-free), 30 days of paid leave per year, and comprehensive health insurance coverage. Additional benefits include family allowances, a relocation package, and opportunities for on-the-job and formal training, including language classes.