Sort by
Refine Your Search
-
Category
-
Employer
- Cranfield University
- University of Nottingham
- ;
- ; The University of Manchester
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Newcastle University
- ; Brunel University London
- ; University of Birmingham
- ; University of Bristol
- ; University of Leeds
- ; University of Nottingham
- ; University of Sheffield
- ; University of Southampton
- ; University of Warwick
- Harper Adams University
- University of Newcastle
- University of Sheffield
- 7 more »
- « less
-
Field
-
. Simulations are suitable to characterise processes in healthy and diseased individuals including stroke patients. Machine learning methods might be considered to accelerate simulations. The project provides a
-
accelerate the world’s transition to carbon-neutral energy systems? Join the Thermofluids Group in the Department of Mechanical Engineering at the University of Sheffield, and embark on a transformative PhD
-
and accelerate aviation decarbonisation efforts from various roles in industry, academia, government, and policy. The interview process is composed of two interviews. Following a first introductory
-
of deep learning models, especially when new training experiences are corrupted. The framework will be validated in robotic control scenarios during EV battery assembly, under process variations such as
-
research which combined efficient optimization and sequential reliability assessment. The project is funded through an EPSRC call to accelerate research outcomes to achieve a prosperous net-zero and is
-
electrodes hold significant promise for improving bioelectronic systems, enhancing energy generation and storage, and accelerating the adoption of renewable energy applications. We invite applications for a
-
industrial settings. From a practical standpoint, new predictive modelling approaches are needed to inform and accelerate industrial process design, as this is an area where much process development occurs
-
techniques and advanced sampling methods to bring a significant advancement in reducing high-fidelity runs to accelerate the engineering design, validation process and improve the robustness of the prediction
-
ensures graduates are prepared to lead and accelerate aviation decarbonisation efforts from various roles in industry, academia, government, and policy. The interview process is composed of two interviews
-
comprehensive understanding of the wider aviation ecosystem. This holistic experience ensures graduates are prepared to lead and accelerate aviation decarbonisation efforts from various roles in industry