Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- ;
- Cranfield University
- ; The University of Manchester
- ; Swansea University
- University of Nottingham
- ; University of Birmingham
- University of Cambridge
- University of Sheffield
- ; University of Warwick
- ; Newcastle University
- ; University of Nottingham
- ; University of Southampton
- ; Cranfield University
- University of Manchester
- ; The University of Edinburgh
- ; University of Surrey
- ; University of Bristol
- ; University of Exeter
- ; City St George’s, University of London
- ; Loughborough University
- ; University of Oxford
- ; University of Sheffield
- Imperial College London
- ; Brunel University London
- ; Edge Hill University
- ; University of Leeds
- ; University of Strathclyde
- ; University of Sussex
- Harper Adams University
- University of Newcastle
- University of Oxford
- ; Aston University
- ; Coventry University Group
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Lancaster University
- ; University of Cambridge
- ; University of East Anglia
- ; University of Greenwich
- ; University of Plymouth
- ; University of Reading
- AALTO UNIVERSITY
- Abertay University
- UNIVERSITY OF VIENNA
- University of Liverpool
- ; Durham University
- ; Imperial College London
- ; Manchester Metropolitan University
- ; Royal Northern College of Music
- ; St George's, University of London
- ; University of Bradford
- ; University of Hertfordshire
- ; University of Portsmouth
- Aston University
- Heriot Watt University
- KINGS COLLEGE LONDON
- Utrecht University
- 46 more »
- « less
-
Field
-
mixed research methods—including behavioural surveys, environmental monitoring, and dynamic thermal modelling—the project aims to generate retrofit strategies that improve energy efficiency, reduce carbon
-
, while simulations are subject to error due to uncertainty in nuclear data and unresolved physical processes e.g. thermal expansion and fine-scale inhomogeneities. Generating independent simulation
-
. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
-
and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies
-
advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow
-
-frequency Joule losses. Litz wire is one of the most promising solutions due to its exceptional ability to reduce AC losses and boost power density. Today's modelling tools are not yet equipped to fully
-
] to further model the elastic airfoil trailing edge and study the interactions of flexible trailing edge with both hydrodynamics and acoustics. The simulation results will be analyzed and compared with
-
to reduce AC losses and boost power density. Today's modelling tools are not yet equipped to fully explore or optimise the flexible structures and manufacturing process of Litz wires. This studentship offers
-
alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic
-
, surgery planning with patient data for surgeons, real-time remote guidance for maintenance in industrial plants, and iterative design simulation for architecture and engineering. However, its wide adoption