Sort by
Refine Your Search
-
Listed
-
Field
-
This self-funded PhD research project aims to develop smart sensors based on low-frequency resonance accelerometers for condition monitoring of ultra-speed bearings. The developed smart sensors will
-
studentship will work with conservation charities and citizen scientists to develop and validate innovative origami-paper eDNA sensors for the rapid detection of chemical and microbial contaminants in river
-
—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
-
AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
amounts of maintenance and operational data, from sensor streams to technical logs, yet much of it remains unstructured, fragmented, and underused. Hidden within these records are insights that could help
-
gas turbine sensor data, if available, will be utilized to validate the developed digital twin in order to estimate non-measurable health parameters of major gas path components, including compressors
-
impacts, interest in river health has grown, pushing monitoring programmes increasingly into the spotlight. At the same time, advancements in sensor technology and deployment mean that much larger ranges