Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Nottingham
- Cranfield University
- ;
- University of Cambridge
- ; Swansea University
- ; University of Nottingham
- ; The University of Manchester
- AALTO UNIVERSITY
- The University of Manchester
- ; City St George’s, University of London
- ; The University of Edinburgh
- ; University of Exeter
- ; University of Southampton
- ; University of Warwick
- University of Newcastle
- ; University of Surrey
- Harper Adams University
- Imperial College London
- University of Bristol
- University of Cambridge;
- University of Oxford
- ; King's College London
- ; University of Cambridge
- ; University of Sheffield
- Abertay University
- Bangor University
- Newcastle University
- Northumbria University;
- The University of Edinburgh
- The University of Manchester;
- UNIVERSITY OF VIENNA
- University of Sheffield
- University of Sussex
- University of Warwick;
- ; Aston University
- ; Brunel University London
- ; Coventry University Group
- ; Cranfield University
- ; Imperial College London
- ; Loughborough University
- ; Newcastle University
- ; St George's, University of London
- ; University of Birmingham
- ; University of Bristol
- ; University of Hull
- ; University of Leeds
- ; University of Plymouth
- ; University of Reading
- ; University of Sussex
- Brunel University London
- Coventry University Group;
- Durham University
- Heriot Watt University
- Heriot-Watt University;
- King's College London;
- Nature Careers
- The Medicines And Healthcare Products Regulatory Agency;
- The University of Edinburgh;
- UCL
- University of Birmingham
- University of Exeter
- University of Glasgow
- University of Hertfordshire
- University of Liverpool
- University of London
- University of Nottingham;
- University of Reading
- University of Strathclyde;
- University of Warwick
- 59 more »
- « less
-
Field
-
reward mechanisms that can incentivise participants to contribute to the training process. Entities should be fairly compensated based on their contributions, which requires developing methods to assess
-
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
-
deep learning methods to enhance the predictions beyond existing data. By incorporating microstructural features into predictive models, the aim is to create a reliable data-driven modelling framework
-
of the Principal Investigator • To work with the Principal Investigator and other colleagues in the research group, as appropriate, to identify areas for research, develop new research methods and extend
-
information from high-quality videos that share content with distorted footage as constraints in the learning process of modelling algorithms. This method uses the characteristics and knowledge embedded in high
-
Experience Experience developing research software using appropriate languages and environements (Python, Julia, Matlab) Knowledge of optimisation problem formulations and solution methods Experience of risk
-
staff are for such technologies and what integral role they should play in evaluating and ensuring an uptake of such technologies. The proposed method will be potentially applied to processes carried out
-
, embrittlement, and cracks. This will be achieved by integrating ultrasonic arrays with inverse modelling methods to interpret historical data. Additionally, the project will explore the failure mechanisms
-
statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big