SW Developer / Experimental Physicist

The position involves research and development of machine learning techniques for track reconstruction in the ATLAS Event Filter system at the High-Luminosity LHC. The role includes investigating ML-based tracking algorithms, benchmarking their performance, and supervising a team within the Next Generation Trigger programme.

Responsibilities

• Conduct research on machine learning (ML) and AI-based approaches for track reconstruction, with a focus on the applicability and performance of these methods in the high pile-up environment of the HL-LHC.
• Investigate and benchmark novel ML-based tracking algorithms and their integration into the ACTS-based EF tracking workflow.
• Contribute to studies of both physics performance and computational performance of the different configurations under study.
• This role includes team supervision responsibilities.

Requirements

• By the application deadline, you have a master’s degree with 2 to 6 years of professional experience since graduation or a PhD with a maximum of 3 years of professional experience since graduation.
• You are not eligible with only a bachelor’s degree.
• You have never had a CERN fellow or graduate contract before.
• Understanding of tracking challenges in high track density environments, such as at the High-Luminosity LHC.
• Experience in the development and application of machine learning or deep learning methods in a physics or scientific computing context.
• Hands-on experience in the development of offline and/or online reconstruction software.
• Ability to lead teams and define directions.
• Machine learning and deep learning frameworks.
• Experience with ML inference deployment.
• Knowledge of ML model training, evaluation, and optimisation, including hyperparameter tuning and performance benchmarking.
• Programming languages: C++ and Python, including software development workflows (Git, Jira).
• Experience with large-scale scientific software frameworks (e.g. ACTS, Athena) is considered an asset.
• Spoken and written English, with a commitment to learn French.

Skills

• Machine Learning
• Deep Learning Models
• ML Inference Deployment
• ML Model Training
• Hyperparameter Tuning
• Performance Benchmarking
• C/C++ Programming
• Python Programming
• Software Development Workflows
• Git
• Jira
• Offline Reconstruction Software
• Online Reconstruction Software
• Large-scale Scientific Software Frameworks
• ACTS Framework
• Athena Framework
• Tracking in High Track Density Environments
• Team Leadership
• Physics Computing

Languages

English, French