Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of Nottingham
- ; Swansea University
- ;
- ; Cranfield University
- ; University of Nottingham
- ; University of Southampton
- University of Bristol
- ; Brunel University London
- ; The University of Manchester
- ; University of Birmingham
- ; University of Bristol
- ; University of Cambridge
- ; University of Oxford
- ; University of Sheffield
- ; University of Surrey
- ; University of Warwick
- King's College London
- UNIVERSITY OF VIENNA
- University of Birmingham
- University of Newcastle
- University of Sheffield
- 12 more »
- « less
-
Field
-
need for materials that can endure extreme conditions while maintaining structural integrity. This Ph.D. project will explore the failure mechanisms in refractory metals and advanced ceramic coatings
-
-based structural integrity model, validated using synchrotron X-ray microtomography and phase contrast imaging, to predict the lifetime of UK’s advanced gas-cooled reactors fuel cladding in storage
-
This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
-
improving the reliability of the prediction of structural performance. This project aims to continue developing the stochastic inference framework by leveraging recent advances in artificial intelligence
-
cyclic loading, varied surface conditions, and exposure to gaseous impurities, and advanced numerical modelling (Finite Element Analysis), this project aims to significantly enhance our understanding
-
-related industries and impacting the aerospace, energy, and automotive sectors. Advanced detection and characterization of these early-stage defects are therefore crucial to supporting both industrial
-
research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle Analysis
-
be used effectively as a performance digital twin to generate high-quality engine performance models and produce required training data for the proposed project. This could be a good starting point for
-
infinite extent models and limited extend data based on trust over particular sets, and naturally create explainable AI structures which can further be analysed from a verification and validation perspective