Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; Swansea University
- ; The University of Manchester
- University of Cambridge
- Imperial College London
- University of Nottingham
- ; Newcastle University
- ; The University of Edinburgh
- ; University of Birmingham
- ; City St George’s, University of London
- ; University of Cambridge
- ; University of Exeter
- ; University of Leeds
- ; University of Oxford
- ; University of Warwick
- UNIVERSITY OF VIENNA
- ; Aston University
- ; Brunel University London
- ; Cranfield University
- ; Loughborough University
- ; Manchester Metropolitan University
- ; University of Bradford
- ; University of Bristol
- ; University of East Anglia
- ; University of Essex
- ; University of Nottingham
- ; University of Reading
- ; University of Sheffield
- ; University of Southampton
- ; University of Surrey
- Abertay University
- Harper Adams University
- KINGS COLLEGE LONDON
- University of Liverpool
- University of Oxford
- 26 more »
- « less
-
Field
-
subsurface and internal temperature distributions. Semi-destructive approaches, such as embedding thermocouples by drilling holes, can provide internal data but often disrupt the process, alter the thermal
-
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
-
, 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
-
: Framework Development: Design and implement a generative deep learning framework for cross-modal integration and analysis, resilient to distribution shifts. Correlation Discovery: Identify interpretable
-
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
-
platform. Training the development digital-twin using real-time data from hardware available Electrical power level studies with developed digital twin to identify visible solutions for distribution electric
-
-driven algorithms which can solve state estimation problems in fluid mechanics, such as inferring the instantaneous state of a fluid’s velocity field from sensors embedded in its boundary. The research
-
of advanced computational techniques. This research will integrate power system modelling, optimisation algorithms, and artificial intelligence (AI) techniques to develop an innovative framework for strategic
-
. This project seeks to advance energy autonomy by optimising power conversion, storage, and distribution in such systems, enabling broader adoption in real-world applications. The project aims to develop a PMC
-
leverage low-precision accelerators for scientific computing by using a number of tricks, known as "mixed-precision" algorithms. Developing such algorithms is far from trivial. We can look at computational