Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- University of Nottingham
- Cranfield University
- ; The University of Manchester
- ; University of Nottingham
- University of Cambridge
- ; University of Warwick
- ; Swansea University
- University of Newcastle
- AALTO UNIVERSITY
- UNIVERSITY OF VIENNA
- ; University of Exeter
- ; University of Reading
- ; University of Surrey
- ; University of Sussex
- University of Sheffield
- ; Aston University
- ; University of Birmingham
- ; University of Cambridge
- ; University of Oxford
- Imperial College London
- ; Brunel University London
- ; City St George’s, University of London
- ; Cranfield University
- ; King's College London
- ; Loughborough University
- ; Maastricht University
- ; Manchester Metropolitan University
- ; Newcastle University
- ; St George's, University of London
- ; Technical University of Denmark
- ; The University of Edinburgh
- ; University of Bristol
- ; University of Greenwich
- ; University of Hertfordshire
- ; University of Hull
- ; University of Leeds
- ; University of Sheffield
- Brunel University
- Durham University
- Harper Adams University
- Heriot Watt University
- KINGS COLLEGE LONDON
- Newcastle University
- THE HONG KONG POLYTECHNIC UNIVERSITY
- University of Liverpool
- University of Oxford
- 37 more »
- « less
-
Field
-
the risk of missed defects. Using the power of Artificial Intelligence (AI), this research aims to: Automate defect detection in complex 3D structural data Enhance diagnostic accuracy and processing speed
-
Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites
-
This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
-
, complexity, and verification needs. By mapping each component to the most appropriate FM tool based on cost-efficiency and expected reliability gains, we aim to construct validation portfolios: automated
-
the understanding of offshore turbulence in spatially varying flows. The focus will be on open channel flow dynamics and controlled experimental studies will be designed and conducted to generate and characterise
-
respond over time (e.g. changing shape), controlled by the arrangement of differential materials within them. The goal of this project will be to develop responsive 4D-printed biomaterial devices for drug
-
-periodic structures, we can precisely control the interaction of radiation with matter, potentially achieving unprecedented timing resolution (sub-70ps) and significantly enhancing signal detection. This PhD
-
region and identify mutations. Develop and optimise bioinformatics tools to detect mutations using positive controls. Apply polygenic risk scores (PRS) to genome-wide SNP data to identify individuals
-
-printed functional devices interact with their environment, responding to stimuli (temperature, light, etc.), and “4D-printed” devices respond over time (e.g. changing shape), controlled by the arrangement
-
energy; thereby minimising farming’s environmental impact. AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical