Sort by
Refine Your Search
-
Employer
- ;
- Cranfield University
- ; Swansea University
- University of Nottingham
- ; The University of Manchester
- ; University of Warwick
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; University of Southampton
- ; University of Sussex
- Abertay University
- ; Brunel University London
- ; Newcastle University
- ; The University of Edinburgh
- ; University of Bristol
- ; University of Cambridge
- ; University of Exeter
- ; University of Leeds
- ; University of Nottingham
- ; University of Reading
- Harper Adams University
- 10 more »
- « less
-
Field
-
become the bottleneck in achieving optimal performance and trustworthiness. This project will focus on how a federated multi-task learning framework can be effectively designed and optimised to address
-
2:1 undergraduate honours degree in a relevant subject and meet our English language requirements. They should have a strong background in physics and/or mathematics (e.g., PDE, optimization) and/or
-
seek optimal trade-offs between compactness and performance, delivering foundational insights into the future of high-performance electric propulsion systems. Funding 3-year PhD tuition fee (for UK home
-
alloys), and additive manufacturing to push performance boundaries. The research will seek optimal trade-offs between compactness and performance, delivering foundational insights into the future of high
-
optimise a ‘Digital Twin’ of the Tees estuary to ensure that the NBS are deployed at locations optimal for performance and longevity while operating within the constraints placed upon deployment by other
-
the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
-
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
-
process. Address blind inverse problems by defining a network to learn distortion functions from data, informing the optimization in the learning process. Refine optimization and learning strategies
-
designing and developing experimental equipment suitable for containing the liquids at the temperatures needed, as well as optimizing the quality of the data obtained, both through experiment design and
-
, and more efficient operations. After all, the greenest energy is the one that’s not spent – and this project aims to unlock just that by refining the way we design and optimize airfoils. The focus