Sort by
Refine Your Search
-
Category
-
Employer
- University of Warwick;
- The University of Manchester
- University of Birmingham
- University of Exeter
- Manchester Metropolitan University
- Durham University
- University of Warwick
- Newcastle University
- University of Bedfordshire
- University of Cambridge
- University of Cambridge;
- University of Oxford
- University of Sheffield
- City St George’s, University of London
- King's College London
- King's College London;
- LIVERPOOL JOHN MOORES UNIVERSITY
- Manchester Metropolitan University;
- Oxford Brookes University
- The Rosalind Franklin Institute
- University of Birmingham;
- University of Bristol
- University of Nottingham
- University of Plymouth
- University of Salford
- University of Sheffield;
- University of Surrey
- University of Sussex
- 18 more »
- « less
-
Field
-
awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
-
and machine learning methodology to help deal with key challenges in developing such models in large-scale observational electronic healthcare record data. These models will be applied to important real
-
temporal resolution. This project aims to overcome these critical limitations by applying advanced machine learning techniques to denoise and improve spatial resolutions. You will run high-fidelity
-
mapping technologies. This PhD would particularly suit candidates with backgrounds in: Artificial intelligence or machine learning Computer science Data science or computational modelling Engineering
-
PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
-
The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide
-
and large models, limiting real-world deployment. This PhD focuses on efficient Physical AI, emphasising data-efficient training, reinforcement learning, continual adaptation and edge deployment
-
machine learning and AI methods to develop clinical decision support systems for high-flow nasal cannula therapy. The project: Concerns around the possible negative consequences of delayed escalation
-
About the project: Machine learning accelerated electronic transport calculations for complex materials Supervisor: Prof. Neophytos Neophytou, University of Warwick Advancements in materials
-
About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat