Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
) sensor data. This will be a small system-on-chip designed to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated
-
-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in
-
to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated into smaller, faster, more energy efficient and cost-effective hardware
-
are poised to re-define our future mobility. However, full autonomy is not possible without all-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms
-
About the Project The future power grid will be a highly complex cyber-physical system, integrating multiple distributed energy resources (DERs) such as solar, wind, marine, and bioenergy alongside
-
—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
-
—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
-
for Pollinator Monitoring: Train and optimise deep learning models for pollinator detection and classification using annotated image datasets. Post-processing object tracking algorithms will be incorporated
-
designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
-
, predictive maintenance algorithms, and digital twin technologies tailored specifically for healthcare, aviation, and sanitation industries. You will identify critical operational pain points within