Applied Physicist

Job Posting Organization:
CERN, the European Organization for Nuclear Research, is a leading scientific research institution established in 195

• It is located in Geneva, Switzerland, and is known for its groundbreaking work in the field of particle physics. CERN employs over 2,500 staff members and collaborates with thousands of scientists from around the world, representing more than 100 nationalities. The organization operates in multiple countries and is dedicated to pushing the frontiers of science and technology, fostering an environment of innovation and collaboration.

Job Overview:
The position of Applied Physicist at CERN involves contributing to the development of machine learning-based Particle-Flow reconstruction for the CMS experiment. This role is critical as it integrates advanced algorithms into the High Level Trigger as part of the Next Generation Trigger project, where timing constraints and real-time performance are essential. The successful candidate 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 (HEP) and the ability to develop and validate ML-based components, ensuring robustness and interpretability in realistic CMS environments. Additionally, the candidate will explore the applicability of these approaches for future collider detectors, lead ML-based reconstruction studies, and design suitable data representations for heterogeneous detector inputs.

Duties and Responsibilities:
The duties and responsibilities of the Applied Physicist include developing ML-based Particle Flow components using TICL inputs in CMS and validating their performance using standard PF and TICL metrics. The candidate will ensure the robustness, interpretability, and debuggability of these components in realistic CMS environments. They will also explore the applicability of these approaches for future collider detectors, building on the FCC framework and the Key4hep ecosystem. Leading ML-based reconstruction studies for CLD and extending the approach to other detector concepts such as ALLEGRO, IDEA, and GRAiNITA is also part of the role. The candidate will be responsible for 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 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 a focus on developing efficient algorithms and integrating different ML techniques into the HEP framework for fast inference. Familiarity with CMSSW and FCCSW is a plus.

Educational Background:
Candidates should have a professional background in Computer Science, Physics, or a related field. They must possess 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. It is important that candidates have never held a CERN fellow or graduate contract before.

Experience:
The position requires candidates to have a professional background in relevant fields, with a Master's degree and a minimum of 2 years of experience or a PhD with up to 3 years of experience. Experience in developing and training machine learning models, particularly in the context of High Energy Physics, is essential. Candidates should also have experience in advanced ML model creation, large-scale and distributed training, and deployment, as the role involves incorporating AI-driven techniques into reconstruction algorithms.

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 for this position is 24 months, with a possibility of extension up to a maximum of 36 months. The working hours are set at 40 hours per week, and the job offers a hybrid working flexibility. 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 compensation includes a monthly stipend ranging from 6372 to 7004 Swiss Francs, which is tax-free, along with 30 days of paid leave per year and additional benefits such as comprehensive health insurance, family allowances, and a relocation package depending on individual circumstances. On-the-job and formal training, including language classes, will also be provided.