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

Join the CAFEIN platform team at CERN and help shape the next generation of federated AI technologies used in some of the world's most advanced scientific environments. In this role, you'll contribute to R&D on state-of-the-art machine learning, designing, implementing, and evaluating advanced algorithms end to end. The work spans large-scale data pipelines and applied solutions in medical AI, anomaly detection, and complex systems modelling from exploratory research through to deployment. Your responsibilities • 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.Your profile • 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.