Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- ;
- ; Swansea University
- University of Nottingham
- ; The University of Edinburgh
- ; Newcastle University
- ; The University of Manchester
- ; University of Birmingham
- ; University of Exeter
- Imperial College London
- University of Cambridge
- ; City St George’s, University of London
- ; University of Cambridge
- ; University of Leeds
- ; University of Southampton
- ; Brunel University London
- ; Cranfield University
- ; Loughborough University
- ; University of Bradford
- ; University of Bristol
- ; University of East Anglia
- ; University of Nottingham
- ; University of Oxford
- ; University of Reading
- ; University of Sheffield
- ; University of Surrey
- ; University of Warwick
- Abertay University
- Harper Adams University
- Newcastle University;
- The University of Manchester;
- University of Bristol
- University of Glasgow
- 23 more »
- « less
-
Field
-
, stress markers, EEG, and ECG — will be collected by VR headsets and IoT devices. ML algorithms will analyse this data to identify trends, project risk factors, and propose tailored treatments. By combining
-
frameworks to ensure the developed processes are compliant, scalable, and environmentally responsible. Multiobjective optimization algorithms will be employed to balance key performance indicators such as
-
novel multi-objective optimisation algorithms, to evaluate metrics such as material circularity, system efficiency, cost, and carbon footprint. The University of Surrey is ranked 12th in the UK in
-
aligning with NQTP Missions 1 and 2 and NQCC Testbed programme, will tailor the developed benchmarking approaches to error-corrected as well as distributed quantum computers, addressing the need for scalable
-
enable a step change in power conversion, transmission and distribution through power electronics based on new materials. At the heart of such systems are power semiconductor devices. The advantages
-
aims: Develop end-to-end protocols for screening selected foods and nutraceuticals. Create advanced strategies for data integration using tailored algorithms and machine learning approaches. Demonstrate
-
lack a direct correlation with process parameters, limiting their ability to predict temperature fields under varying process conditions. The transferred arc energy distribution becomes particularly
-
for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
-
. Measurements on fuel injectors relevant to current design standards have shown significant influence of injector aerodynamics on the dispersed spray distribution and the importance of prefilming fuel flows
-
analytics, anomaly detection, and embedded redundancy to enhance system resilience. Students will focus on creating adaptive algorithms and hardware implementations that enable real-time diagnostics and