Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of East Anglia
- University of Nottingham
- AALTO UNIVERSITY
- Loughborough University
- The University of Manchester
- University of Sheffield
- ;
- Bangor University
- KINGS COLLEGE LONDON
- The University of Manchester;
- University of Birmingham
- University of Birmingham;
- University of Cambridge
- University of Cambridge;
- University of Warwick
- Edinburgh Napier University;
- Oxford Brookes University
- The University of Edinburgh;
- University of Bristol
- University of East Anglia;
- University of Nottingham;
- University of Oxford
- University of Sheffield;
- University of Surrey
- ; Coventry University Group
- ; The University of Manchester
- ; University of Exeter
- European Magnetism Association EMA
- Harper Adams University
- King's College London
- King's College London;
- Liverpool John Moores University
- Loughborough University;
- Manchester Metropolitan University;
- Nature Careers
- Newcastle University
- The University of Edinburgh
- UCL
- Ulster University
- University of Essex
- University of Exeter
- University of Exeter;
- University of Leeds
- University of Liverpool
- University of Newcastle
- University of Warwick;
- 37 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
-
railway earthworks. Additionally, the project will integrate environmental data through data fusion and develop automated machine learning tools for anomaly detection and risk assessment. The effectiveness
-
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
-
processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
-
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
-
University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
-
. 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
-
motivated PhD student to join our interdisciplinary team to help address critical challenges in high-speed electrical machine design for electrified transportation and power generation. Together, we will make
-
the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
-
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