Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of Nottingham
- ;
- ; City St George’s, University of London
- ; The University of Manchester
- ; University of Nottingham
- ; Swansea University
- ; University of Exeter
- University of Cambridge
- ; University of Southampton
- ; University of Surrey
- AALTO UNIVERSITY
- KINGS COLLEGE LONDON
- The University of Manchester
- The University of Manchester;
- University of Bristol
- University of Cambridge;
- University of Newcastle
- University of Sheffield
- ; Brunel University London
- ; Coventry University Group
- ; Cranfield University
- ; Durham University
- ; Newcastle University
- ; The University of Edinburgh
- ; UCL
- ; UWE, Bristol
- ; University of Birmingham
- ; University of Bristol
- ; University of Greenwich
- ; University of Leeds
- ; University of Oxford
- ; University of Reading
- ; University of Warwick
- Abertay University
- Harper Adams University
- Imperial College London
- King's College London;
- Loughborough University
- Nature Careers
- Oxford Brookes University
- The University of Edinburgh;
- UCL
- University of Birmingham
- University of Exeter
- University of Liverpool
- University of Nottingham;
- University of Sheffield;
- University of Surrey
- University of Warwick
- 40 more »
- « less
-
Field
-
failures before they occur, enabling proactive maintenance strategies. Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing
-
health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities
-
. when do we stop modelling? How do we track / score the quality of the model? What is the required level of quality over time? How can quality be brought to the required level? Can Machine Learning, Large
-
overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
-
to achieve, at least a 2.1 honours degree or a master’s in a relevant science or engineering related discipline. Applicants should have strong background in Machine Learning and Deep Learning. To apply, please
-
should have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge
-
We are pleased to announce PhD studentship project in “Advanced Composites Development for Hyper-velocity Impact Protection of Space Satellites Structures”. This is an exciting PhD research
-
learning. The project involves a collaborative team, including a postdoctoral researcher and a PhD student, with specific objectives: Define and acquire a comprehensive database of high-quality video priors
-
Supervisory Team: Leonardo Aniello, Han Wu PhD Supervisor: Leonardo Aniello Project description: Blockchain and Federated Learning (FL) are two emerging technologies that, when combined, offer a
-
Machine Learning-based diagnostics and prognostics digital twin system will be developed, aiming to provide fast and reliable predictions of the health of gas turbine engines. Non-confidential operational