Deep Learning Developer

The Deep Learning Developer will contribute to the design and training of transformer-based architectures for particle physics data within the TURING project, collaborating with multidisciplinary experts and CERN experimental physics community. The role involves developing robust prototypes and leading performance evaluation and use case validation tasks for foundation models in High Energy Physics detectors.

Responsibilities

• Take an active role within the TURING project, leading the performance evaluation and use case validation tasks.
• Develop a robust prototype capable of generalising across multiple detector use cases.
• Contribute to tightening the collaborations with the CERN experimental physics community on the topic of foundation models for High Energy Physics.

Requirements

• You are a national of a CERN Member State or Associate Member State, excluding Pakistani, Lithuanian, Latvian, Cypriot and Croatian nationals due to Associate Membership Agreement ceilings.
• 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.
• Proven experience developing, training, and deploying deep learning models in production environments, with hands-on expertise in neural network architectures, large-scale data processing, model optimisation, and performance evaluation.
• Your studies focused on Data Science, Mathematics or Physics.
• Strong proficiency in Python and deep learning frameworks (PyTorch and/or TensorFlow), machine learning algorithms, data analysis libraries (NumPy, Pandas), GPU-based training, software engineering best practices, Git version control, Docker, and MLOps tools for experiment tracking and model deployment.
• Spoken and written English, with a commitment to learn French.

Skills

• Deep Learning Model Development
• Neural Network Architectures
• Large-scale Data Processing
• Model Optimisation
• Performance Evaluation
• Python Programming
• PyTorch
• TensorFlow
• Machine learning algorithms
• Data Analysis with NumPy
• Data analysis with Pandas
• GPU-Based Training
• Software Engineering Practices
• Git Versioning
• Docker Containers
• MLOps Tools
• Experiment Tracking
• Model Deployment

Languages

English, French