Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- ; City St George’s, University of London
- University of Nottingham
- ; University of Nottingham
- University of Cambridge
- University of Sheffield
- ;
- ; Swansea University
- ; The University of Manchester
- ; University of Exeter
- AALTO UNIVERSITY
- The University of Manchester
- University of Cambridge;
- ; University of Southampton
- Bangor University
- KINGS COLLEGE LONDON
- The University of Manchester;
- University of Bristol
- University of Newcastle
- University of Surrey
- ; Brunel University London
- ; Coventry University Group
- ; Newcastle University
- ; UCL
- ; University of Leeds
- ; University of Warwick
- Abertay University
- Harper Adams University
- King's College London;
- Liverpool John Moores University
- 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 Oxford
- University of Sheffield;
- University of Warwick
- 32 more »
- « less
-
Field
-
to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
-
members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability
-
integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
-
models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
-
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
-
validation with end-users. The student will have access to specialised training in quantum security and advanced machine learning. The self-funded nature of the project affords the unique flexibility to pursue
-
, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
-
, potentially including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during
-
. The successful candidate will develop advanced skills in multi-modal sensor fusion, signal processing, machine learning, and integrity assessment, as well as transferable abilities in critical thinking, project
-
considered. Qualifications/Skills PhD degree in a programme relevant to human-computer interaction and/or critical computing, ideally in Computer Science, Industrial Engineering, Interaction Design, or a