Research Opportunities
Anomaly Detection and Explanation
Work description
The work programme integrates Explainable AI research with industrial predictive maintenance applications: Study the state of the art in predictive maintenance and XAI. Study XAI surrogate models to explain failures detected by unsupervised models on sensor data Assess explanation quality and robustness through experiments on real-world, imbalanced data. Writing articles for journals or conferences.
Academic Qualifications
Master in data analytics or similar areas.
Minimum profile required
Strong knowledge in machine learning and explainable AI.Knowledge of root cause analysis.Experience with Python.
Preference factors
Proven experience in root cause analysis, demonstrated by publications in conferences and journals.
Application Period
Since 29 Jan 2026 to 11 Feb 2026
Centre
Artificial Intelligence and Decision Support