Anomaly Detection and Explanation
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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 AIKnowledge of root cause analysisExperience with Python
Preference factors
Proven experience in root cause analysis, demonstrated by publications in conferences and journals.
Application Period
Since 23 Oct 2025 to 05 Nov 2025
[Open soon]
Centre
Artificial Intelligence and Decision Support