Computer Science
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Work description
Algorithms for Federated Anomaly Detection over Data Streams: (i) stream-based algorithms definition and implementation in the federated framework; (ii) distributed learning with computational and energy constraints; (iii) compliance with privacy regulations and best practices. Implementation of evaluation techniques for data streams and federated learning: (i) statistical metrics; (ii) system metrics. Writing articles for journals or conferences.
Academic Qualifications
Master in Informatics or similar area
Minimum profile required
Strong knowledge in federated learning.Knowledge in data streams, online learning e anomaly detection.Experience with Python, River, and Flower framework.
Preference factors
Proven experience in federated learning, 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