Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of Nottingham
- ;
- ; Cranfield University
- ; University of Southampton
- University of Newcastle
- ; The University of Edinburgh
- ; University of Cambridge
- ; University of Nottingham
- AALTO UNIVERSITY
- Abertay University
- Imperial College London
- Kingston University
- The University of Edinburgh;
- The University of Manchester
- UCL
- UNIVERSITY OF VIENNA
- University of Bristol
- University of Cambridge
- University of Exeter
- University of Glasgow
- University of Oxford
- 12 more »
- « less
-
Field
-
This PhD opportunity at Cranfield University invites ambitious candidates to explore the frontier of energy-efficient intelligent systems by embedding AI into low-power, long-life hardware platforms
-
Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
-
and testing new bioinformatic pipelines to analyse important public health pathogens. The team comprises research software engineers, bioinformatic engineers, biostatistical researchers, clinical
-
This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
-
This PhD opportunity at Cranfield University invites candidates to pioneer research in embedding AI into electronic hardware to enhance security and trustworthiness in safety-critical systems
-
Supervisory Team: Dr. Jie Yuan, Dr. David Toal PhD Supervisor: Jie Yuan Project description: Robust design is crucial to ensure the durability and reliability of aerospace components throughout
-
methods in the past. A piece of comprehensive computer software, Pythia with the corresponding capabilities have been developed and tested successfully in several industrial applications. The software can
-
metrics during both standard operation (primarily governed by system reliability) and extreme events (primarily governed by robustness and restoration). This will be achieved by building on previous
-
Supervisory Team: Prof Neil Sandham PhD Supervisor: Neil Sandham Project description: This project is focused on scale-resolving simulations (large-eddy and direct numerical simulation) combined
-
candidates are invited to apply promptly as selections will be made on a rolling basis. Ideal candidates would have a strong background in Computer Sciences, Software Engineering, Artificial Intelligence