Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- University of Nottingham
- ; Swansea University
- ; The University of Manchester
- ; University of Birmingham
- ; University of Nottingham
- ; University of Warwick
- ; University of Southampton
- University of Cambridge
- University of Newcastle
- ; Cranfield University
- ; Newcastle University
- ; University of Reading
- ; Brunel University London
- ; City St George’s, University of London
- ; Loughborough University
- ; Manchester Metropolitan University
- Harper Adams University
- Imperial College London
- UNIVERSITY OF VIENNA
- University of Manchester
- ; Edge Hill University
- ; The University of Edinburgh
- ; University of Bristol
- ; University of Exeter
- ; University of Oxford
- ; University of Sheffield
- ; University of Surrey
- ; University of Sussex
- Abertay University
- Brunel University
- Heriot Watt University
- University of Sheffield
- ; Coventry University Group
- ; Durham University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; King's College London
- ; Lancaster University
- ; Queen's University Belfast
- ; St George's, University of London
- ; UWE, Bristol
- ; University of Cambridge
- ; University of East Anglia
- ; University of Greenwich
- ; University of Hertfordshire
- ; University of Hull
- ; University of Kent
- ; University of Leeds
- ; University of Stirling
- ; University of Strathclyde
- Durham University
- KINGS COLLEGE LONDON
- Nottingham Trent University
- University of Liverpool
- University of Oxford
- 46 more »
- « less
-
Field
-
of the challenges is fault detection and diagnosis of bearings subject to low (rotational) speed. As vibration/acoustic signals generated by the faults of low-speed bearings are very weak and often covered by strong
-
objective is to find the best way to embed simple partial differential equations into AI-based models to solve fluid sensing problems in a robust and efficient manner. Your role may include developing new
-
. The object of our studies is to gain a fundamental understanding of this incredible family of glycans, opening the potential to use that knowledge to design new drugs, new biomaterials or to identify new
-
the project. Project Objectives Characterise the surface properties of reclaimed carbon and glass fibres from different sources and with varying processing histories. Investigate suspension behaviour, including
-
AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
-
, improving the speed and precision of emergency response systems. The objectives include: Collaborate with YAS to curate a high-quality dataset of emergency call recordings, annotated with corresponding
-
ligand(s). The 3D organisation of these domains is therefore critical for their function. The object of our studies is to gain a fundamental understanding of this incredible family of glycans, opening
-
Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly
-
alongside traditional coal fired power stations and nuclear energy generation. Revolutionary changes to power conversion is indispensable if these carbon emissions targets are to be met. The objective is to
-
healing, haemostasis promotion and water treatments). The objectives of this project focus on evaluating the antibiofilm efficiencies of different Chitosan derivatives with various polymer sizes and to