Computer Science
[Open soon]
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's degree in Computer Science, informatics, or related fields
Minimum profile required
Experience with deep learning and transformers.Strong knowledge of federated learning.Knowledge of data pipelines, online learning, and anomaly detection.Experience with the Python frameworks River and Flower.Currently pursuing a Ph.D. in Computer Science, Informatics or a related field
Preference factors
Proven experience in federated learning, demonstrated by publications in conferences and journals.
Application Period
Since 14 May 2026 to 27 May 2026
[Open soon]
Centre
Artificial Intelligence and Decision Support