Sort by
Refine Your Search
-
industrial SBSP experts, the candidate will explore the nonlinear structural dynamics of LSSs to fully understand the complexity of their control. They will use this foundation to explore idealised and
-
AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
, computer vision, and data analysis using industry-standard tools such as Python, MATLAB, and deep learning frameworks. The student will enhance their ability to manage complex, interdisciplinary research
-
operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
-
Develop practical, industry-transforming technology in this hands-on PhD program focused on immediate industrial applications. This exclusive opportunity places you directly at the interface between
-
for Security Operations Centres (SOCs) while pioneering strategies for quantum-era resilience. This project sits at the intersection of Artificial Intelligence, Cybersecurity, and Explainable Computing. It
-
navigation framework aiming for applications requiring position assurance under the most complex navigation scenarios and increased uncertainty in available navigation information. The project contributes
-
trustworthy operation of navigation systems in complex, GNSS-denied scenarios. The ultimate goal is to provide the navigation research community and industry with tools and methods that ensure continuous, high
-
: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
-
/or increase in efficiency. Additive manufacturing (AM) could help increase the efficiency of the GTs by enabling complex designs. AM has been used for static GT components, however the use for high
-
state-of-the-art high heat flux testing, simulating the extreme environments of fusion reactors. Harness advanced computational tools to model complex particle-material interactions and predict material