Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- Cranfield University
- ;
- ; University of Oxford
- ; Cranfield University
- ; Swansea University
- ; The University of Manchester
- ; University of East London
- ; University of Sussex
- ; University of Warwick
- Curtin University
- DAAD
- Newcastle University
- Technical University of Denmark
- Technical University of Munich
- UNIVERSITY OF EAST LONDON
- University of East London
- University of Twente
- University of Utah
- 8 more »
- « less
-
Field
-
solutions by enabling systems to detect anomalies, predict failures, and initiate corrective actions autonomously. This approach enhances system resilience and reduces maintenance costs, particularly in
-
into areas such as AI-driven verification, predictive maintenance, and compliance assurance, aiming to enhance system reliability and safety. Situated within the esteemed IVHM Centre and supported by
-
platforms can unify production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current
-
monitoring. While ML-driven industrial condition monitoring offers significant advantages in predictive and preventive maintenance, current research often relies on simplified, laboratory-based data. This PhD
-
this, there are further sub-objectives during the investigation to achieve this goal: Predict thermal warpage effects on a supersonic intake at different flight times, coupled to a numerical model for the downstream
-
that limit their predictability and broader application. You will investigate the underlying mechanical principles—including contact stiffness, constraint geometry, and material interactions, in particular
-
achievement of net-zero greenhouse gas emissions targets. Because of this, peatland restoration is a conservation priority in the UK and internationally. However, as ecosystems dependent on the maintenance
-
perpetuation (or maintenance/persistence); to build ML models that include the heart’s physical properties to find patterns in the data and predict which areas in the heart drive AF. This project will explore
-
predictive maintenance. Gas turbine diagnostics and prognostics has been progressed quickly in recent years and are crucial technologies to predict the health of gas turbine systems and support the predictive
-
tetrapeptides possible from the 22 natural amino acids alone, with further synthetic modifications possible) means that it is imperative that we can predict and study in vitro which compounds are likely to be