Data Scientist (TE-DPS-AIM-2026-76-GRAP)

• Translate real-world challenges, medical data analysis, anomaly detection, complex systems modelling, into well-defined ML problems.
• Design, implement, and evaluate state-of-the-art ML models using Python and frameworks such as PyTorch, TensorFlow, JAX, scikit-learn, and Hugging Face.
• Contribute to the research and development of federated learning methods, agentic AI, and applied ML solutions for tabular, image and signal data.
• Develop applied ML solutions for diverse domains including medical imaging, anomaly detection, and predictive maintenance.
• Perform exploratory data analysis, feature engineering, and visualisation to support model development and identify new use cases.
• Document methods, write clear reports, publish findings, and communicate results within multidisciplinary R&D teams.
• This role includes team supervision responsibilities.

Do you have experience in Research?, Do you have a Master's degree?, * Strong background in machine learning, statistics, and data science fundamentals.

• Excellent proficiency in Python for ML research and experimentation, with hands-on experience with major frameworks and libraries (PyTorch, TensorFlow, JAX, scikit-learn, Hugging Face).
• Demonstrated experience designing, implementing, and evaluating state-of-the-art ML algorithms, including transformer-based and agentic architectures.
• Experience conducting ML experiments, benchmarking models, and interpreting results across diverse data modalities (tabular, medical imaging and signal data).
• Practical exposure to federated learning concepts and privacy-preserving or distributed ML approaches is a plus.
• Strong scientific mindset with a track record of reading, understanding, and contributing to research literature.
• Good communication skills to document methods, present results, and collaborate within multidisciplinary R&D teams.

Skills

• Expert knowledge of Python, deep learning frameworks (PyTorch, TensorFlow, JAX, Hugging Face), and machine learning algorithms.
• Professional knowledge of anomaly detection algorithms, LLMs, RAG systems, Generative AI, and agentic AI approaches.
• Professional knowledge of image segmentation techniques and Graph Neural Networks (GNNs).
• Familiarity with federated learning tools and distributed ML environments.
• Strong communication skills for documenting methods, presenting results, and collaborating in multidisciplinary R&D teams.
• Knowledge of particle accelerator systems or healthcare applications would be an advantage.
• Spoken and written English, with a commitment to learn French.

Eligibility criteria:

• You are a national of a CERN Member or Associate Member State.
• You have a professional background in Data science, IT, Mathematics (or a related field) and have 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.

• You have never had a CERN fellow or graduate contract before.

• A monthly stipend between 6372-7004 Swiss Francs per month (tax free) depending on your degree.
• 30 days of paid leave per year plus 2 weeks annual closure.
• Coverage by CERN's comprehensive health insurance scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund.
• Family, child and infant monthly allowances depending on your individual circumstances.
• A relocation package (installation grant and travel expenses) depending on your individual circumstances.
• Possibility to extend your contract up to 36 months.
• On-the-job and formal training including language classes.

Imagine taking part in the largest scientific experiment in the world. CERN needs more than physicists and engineers - if you're a student, a graduate, just starting your career or an experienced professional, whatever your field of expertise, CERN could be your next opportunity.