Sort by
Refine Your Search
-
Category
-
Employer
- Cranfield University
- ;
- ; Swansea University
- ; University of Reading
- University of Manchester
- University of Nottingham
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; The University of Manchester
- ; University of Exeter
- ; University of Strathclyde
- Imperial College London
- Newcastle University
- 2 more »
- « less
-
Field
-
. Low-power AI is crucial in this context, enabling continuous link monitoring and decision-making without exhausting limited satellite energy resources. The AI models will predict potential failures and
-
, with applications in search-and-rescue, environmental monitoring, and planetary exploration. Applicants should have a First or strong Upper Second-class honours degree (2:1 with 65% average
-
the integrity of infrastructure such as pipelines and process plants. Traditional inspection and monitoring methods often face limitations when dealing with complex pipework and constrained geometries
-
of artificial intelligence (AI) nowadays, it has become possible to develop a fast-response AI-based condition monitoring system for gas turbine engines. The objective of the project is to develop novel AI-based
-
the foundation of computer vision, monitoring, and control solutions. However, real applications of AI have typically been demonstrated under highly controlled conditions. Battery assembly processes can be
-
Type of award Studentship Managing department Faculty of Humanities Value Subject to residential eligibility status, the award covers: Tuition fees Maintenance stipend (the annual maintenance
-
are among the world leaders in through-life approaches for high value systems, Condition monitoring, Damage tolerance, Asset management. TES was developed with the support of EPSRC grant of £ 11 million with
-
experts in the prognostics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University. About the host University/Centre Cranfield is an
-
are among the world leaders in through-life approaches for high value systems, Condition monitoring, Damage tolerance, Asset management. TES was developed with the support of EPSRC grant of £ 11 million with
-
optimal operating conditions and followed by surface analysis techniques (e.g. Scanning electron microscope, X-ray diffraction for residual stress measurements, Electron Back-Scattered Diffraction and