GANIL’s SPIRAL1 and SPIRAL2 facilities produce complex data that remain hard to interpret. SPIRAL2 faces instabilities in its superconducting cavities, while SPIRAL1 requires reliable isotope identification under noisy conditions.
This PhD will develop observer-based interpretable AI, combining physics models and machine learning to detect, explain, and predict anomalies. By embedding causal reasoning and explainability tools such as SHAP and LIME, it aims to improve the reliability and transparency of accelerator operations.