Sort by
Refine Your Search
-
AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
-
uncertainty and dynamic conditions. In complex electronic systems, ensuring reliability and minimizing downtime are critical challenges. AI-driven fault diagnosis and self-healing electronics offer innovative
-
of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
-
. The control systems developed will be designed to handle these complex dynamics, feeding directly into industrial SBSP and related designs, thus enabling their longer-term plans for commercial SBSP capable
-
substrate, enabling the layer-by-layer construction of complex 3D objects. The temperature field created by the interaction between the electric arc and the material is a critical factor influencing the
-
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
-
knowledge co-evolution and addressing complex challenges in a super-intelligent society. This project is situated within the rapidly evolving field of Cyber-Physical-Social Systems (CPSS), which is of
-
categories for a better capability of managing the uncertainty related to system complexity and data availability to achieve more accurate RUL estimations The student will have the opportunity to work with
-
This is a self-funded opportunity relying on Computational Fluid Dynamics (CFD) and wind tunnel testing to further the design of porous airfoils with superior aerodynamic efficiency. Building
-
, high-speed vibration monitoring in complex assemblies, and improved measurement capabilities in two-phase flow systems such as cryogenic fuels and hydrogen/ammonia-based configurations. This project