Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- ;
- Cranfield University
- ; The University of Manchester
- University of Nottingham
- ; Swansea University
- ; University of Birmingham
- University of Cambridge
- ; University of Warwick
- ; University of Nottingham
- University of Sheffield
- ; University of Southampton
- ; Newcastle University
- ; Cranfield University
- ; University of Bristol
- ; University of Exeter
- ; University of Oxford
- ; University of Surrey
- ; City St George’s, University of London
- ; Loughborough University
- ; University of Sheffield
- ; The University of Edinburgh
- ; University of Leeds
- Imperial College London
- ; Brunel University London
- ; 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
- ; University of Cambridge
- ; University of East Anglia
- ; University of Greenwich
- ; University of Reading
- ; University of Strathclyde
- AALTO UNIVERSITY
- Abertay University
- UNIVERSITY OF VIENNA
- University of Liverpool
- ; Durham University
- ; Imperial College London
- ; Manchester Metropolitan University
- ; Midlands Graduate School Doctoral Training Partnership
- ; Royal Northern College of Music
- ; St George's, University of London
- ; Technical University of Denmark
- ; University of Bradford
- ; University of Plymouth
- ; University of Portsmouth
- Aston University
- Heriot Watt University
- KINGS COLLEGE LONDON
- Utrecht University
- 44 more »
- « less
-
Field
-
heavier than their fossil fuel powered counterparts. A framework that can accurately model complex dynamics and generate projections for future scenarios is essential for understanding the impact of changes
-
) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including
-
computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
-
for downstream tasks. In this project, you will develop novel unsupervised machine learning methods to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a
-
scientists on site and around 300 researchers in Vienna. The Brukner group currently consists of an international team of 9 young researchers (Master, PhD, and Postdoc levels). For more information, please
-
scientists on site and around 300 researchers in Vienna. The Brukner group currently consists of an international team of 9 young researchers (Master, PhD, and Postdoc levels). For more information, please
-
: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
-
experimentation and finite-element modelling. Research themes would be flexible including green steel formability under the EPSRC ADAP‑EAF programme for automotive and packaging applications; or micromechanical
-
, multi-source learning is used to integrate diverse patient populations to build robust models, but having to protect sensitive information. Various modern ML paradigms are proposed to address the diverse
-
to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a distribution of normal cardiac anatomy and function (including motion) from healthy subjects. By