Description

In this role, you will be at the forefront of real-time Machine Learning, designing and training models to enhance physics selections. Your work will involve integrating these models into FPGAs and collaborating with a diverse team to push the boundaries of data processing in high-energy physics.

Requirements

• •Experience developing and applying Machine Learning algorithms for physics or scientific data analysis
• •Familiarity with Fast ML or hardware-constrained ML techniques
• •Knowledge of physics analysis or physics event reconstruction methods
• •Experience with Trigger and Data Acquisition systems, including hardware architectures
• •Practical experience with software development, GitHub/GitLab, Continuous Integration, MLOps
• •Basic knowledge of FPGA design including HDLs and/or High Level Synthesis
• •Skills in Machine Learning and Fast Machine Learning
• •Physics Data Analysis and Reconstruction
• •Trigger Systems and Data Acquisition
• •MLOps, Continuous Integration and CI/CD Pipelines
• •FPGA Design and Programming
• •Hardware Description Languages and High-Level Synthesis
• •Spoken and written English, commitment to learn French
• •National of a CERN Member or Associate Member State
• •Master's degree with 2 to 6 years of post-graduation professional experience or PhD with no more than 3 years of post-graduation professional experience
• •No previous CERN fellow or graduate contract

Tasks

• •Design and train ML models for CMS Phase-2 Level-1 Trigger
• •Optimize information transport across the multi-algorithm system
• •Develop and integrate ML models into FPGAs
• •Test ML models using RTL/HLS components and software emulators
• •Demonstrate physics performance gains and present results
• •Implement MLOps practices for reproducible training and validation
• •Scale workflows for deployment of ML-based trigger algorithms
• •Collaborate with CMS, CERN departments, and external ML research institutes

Tools & Technologies

Benefits