Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- University of Nottingham
- ; Swansea University
- ; The University of Manchester
- ; University of Birmingham
- University of Sheffield
- ; University of Southampton
- ; University of Surrey
- University of Cambridge
- ; City St George’s, University of London
- ; Cranfield University
- ; Newcastle University
- University of Newcastle
- ; The University of Edinburgh
- ; University of Exeter
- ; University of Nottingham
- ; Loughborough University
- ; University of Bristol
- ; University of Oxford
- ; University of Warwick
- AALTO UNIVERSITY
- Imperial College London
- The University of Manchester
- University of Bristol
- University of Oxford
- ; Brunel University London
- ; University of Cambridge
- ; University of East Anglia
- ; University of Sheffield
- Abertay University
- Harper Adams University
- KINGS COLLEGE LONDON
- ; Aston University
- ; Coventry University Group
- ; Durham University
- ; Imperial College London
- ; Manchester Metropolitan University
- ; St George's, University of London
- ; University of Greenwich
- ; University of Leeds
- ; University of Plymouth
- ; University of Reading
- ; University of Strathclyde
- Coventry University Group;
- King's College London;
- Loughborough University
- Manchester Metropolitan University
- Newcastle University
- The University of Edinburgh
- The University of Edinburgh;
- The University of Manchester;
- UCL
- UNIVERSITY OF VIENNA
- University of Birmingham
- University of Cambridge;
- University of Exeter
- University of Glasgow
- University of Greenwich
- University of Liverpool
- University of London
- University of Nottingham;
- University of Strathclyde;
- University of Warwick
- University of Warwick;
- 55 more »
- « less
-
Field
-
targets the development of advanced coatings to prevent cell-to-cell propagation during runaway events. It combines experimental studies, numerical modelling, and real-world burner rig testing, culminating
-
marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
-
: 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
-
of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
-
correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
-
verification methodology and corresponding toolchain to detect and mitigate such threats to CPS at the design time making the CPS resilient-by-design. Typically, CPS are modelled as hybrid systems, comprising
-
. Using gastruloids as a model system with which to study GAG structure/function relationships. Generating gastruloids from induced pluripotent stem cells (iPSCs) to create in vitro models for studying
-
of the assembly of these complex microbial communities using ecological theory and mathematical models. The questions we address are: (1) how does the microbial community change during cultivation
-
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
-
determine the impact of community acquired pneumonia that requires hospitalisation has on the quality of life of patients. The final stage will be to design a generic economic model to evaluate any new