Study level
- PhD
Faculty/School
Faculty of Engineering
Topic status
We're looking for students to study this topic.
Supervisors
- Position
- Professor in Electrical Engineering
- Division / Faculty
- Faculty of Engineering
Overview
Future robotics systems are likely to benefit from having an ability to self-diagnose self-failure or the presence of anomalous situations (so that they can switch to fallback or fail-safe modes). Example situations include subtle sensor or actuator failure and cyber security or physical intruder detection.
Such low signal-to-noise anomaly detection or self-diagnose problems can be understood using powerful mathematical and statistical tools which QCR has a rich history of advancing through collaboration with industry partners and publication in premium international venues.
Skills and experience
These PhD projects suit students interested in working on novel statistical and information theory tools. Potentially involves learning about dynamic programming on infinite dimensional spaces to understanding challenging optimal stopping problems (a type of decision problem), and practical implementation of non-linear filtering computations.
Contact
Contact the supervisor for more information.