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Postdoc in Machine learning and Cyber-physical systems security | EURAXESS

The emerging technologies on machine learning offer the potential of acquiring and processing tremendous amount of data from the Cyber-Physical Systems CPS. More and more people are engaged dynamically via multiple, heterogeneous data sources while many systems are still running in isolation. It becomes significant to analyze, understand, and utilize the knowledge in CPS, in order to provide more intelligent systems and applications to end users, who are typically dynamically interconnected through networks.

Promising computing paradigms and advanced technologies, relating to machine learning, distributed smart sensing, heterogeneous big data analytics, and deep learning, etc. Despite the related technologies are advancing at an unprecedented pace, many challenges remain in bridging the CPS.

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This special session aims to collect the latest original contributions in machine learning and its applications in CPS, and offer new ideas, experiences and discussions by experts in this field. We encourage the submission of papers with new theory, analysis, methods, and applications.

Machine Learning for Cyber Physical Systems

Selected papers, after further extensions and revisions, will be recommended to journal special issues. Paper Submission Due: March 01, Acceptance Notification Due: Apr 01, Final Manuscript Due: Jun 01, Back to main congress: Cybermatics Organizations :.

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Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Recent advances in AI and implications for cyber-physical systems

His research interests are in the field of machine-learning, data-fusion and data-driven condition monitoring. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. Skip to main content Skip to table of contents.

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