Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of Nottingham
- ;
- ; Swansea University
- ; The University of Manchester
- ; University of Warwick
- ; Cranfield University
- ; University of Nottingham
- ; University of Sheffield
- ; University of Southampton
- University of Bristol;
- University of Newcastle
- University of Sheffield
- ; Brunel University London
- ; University of Birmingham
- ; University of Bristol
- ; University of Cambridge
- ; University of Oxford
- ; University of Strathclyde
- ; University of Surrey
- Newcastle University;
- UNIVERSITY OF VIENNA
- University of Birmingham;
- University of Nottingham;
- 14 more »
- « less
-
Field
-
machine structures, together with AI-driven optimization frameworks for diverse applications while considering LCA metrics. The success of this project could serve as a model for other energy-related
-
changes (so called swelling). Swollen batteries are at risk of rupturing which may significantly shorten their lifetime. Development of advanced computer models is critical for understanding and
-
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
-
). Access to cutting-edge facilities, including advanced microscopy, controlled environment growth rooms, genomics, proteomics, and metabolomics platforms. Opportunities to work across model and crop species
-
methodology to deliver simulations over a wider operating regime than currently possible. The realisation of this aim involves the advancement of the modelling of the air film and the foil, and their
-
model of reaction barriers. This will enable the development of more accurate and advanced high-throughput reaction network discovery and by-product prediction. Background Typical drug molecules can
-
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
-
research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle Analysis
-
-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