Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of Nottingham
- KINGS COLLEGE LONDON
- Loughborough University
- University of Exeter;
- AALTO UNIVERSITY
- Imperial College London;
- The University of Edinburgh
- University of Sheffield
- University of Birmingham
- University of Cambridge;
- Imperial College London
- Newcastle University
- The University of Manchester;
- University of Birmingham;
- University of Exeter
- ;
- Bangor University
- Brunel University London
- Edinburgh Napier University;
- King's College London
- King's College London Department of Engineering
- Lancaster University
- The University of Manchester
- UNIVERSITY OF VIENNA
- University of Bristol
- University of East Anglia
- University of East Anglia;
- University of Glasgow
- University of Greenwich
- University of Hertfordshire
- University of Manchester
- University of Newcastle
- University of Nottingham;
- University of Oxford
- University of Oxford;
- University of Strathclyde;
- University of Surrey
- University of Warwick;
- 29 more »
- « less
-
Field
-
. The project is co-sponsored by Spirent Communications, a world leader in navigation and testing technology. Spirent will provide advanced simulation tools, expert support, and industry placements to help make
-
elements like Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) to secure hardware components. Embedded Trust Protocols: Design protocols that establish and maintain trust within
-
apparatus equipped with thermocouples and thermal imaging to simulate realistic runaway events. Top-performing coatings will be validated in situ on live EV cells under controlled runaway conditions. Dr
-
predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
-
Verification Tools: Develop AI algorithms that automate the verification process, ensuring systems meet required safety and performance standards. Health Monitoring Algorithms: Implement AI-based monitoring
-
collaborative team engaged in a range of research projects in marine hydrodynamics, both computational and experimental. This is a fully on-site role, with work taking place in the office and laboratory. We
-
with a first class or upper second-class degree in engineering, physics, applied mathematics or a related field. A solid foundation in fluid dynamics and heat transfer, and experience with computer
-
, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
-
the development of a low fidelity pump model that accounts for unstable and multi-phase flow behaviour through high fidelity simulations. This will be used to develop an integrated fuel system model that will
-
in healthcare service and opportunities for identification of such deviations using computer vision approaches. It will demonstrate how deviation data can be used in computer-based simulation models