Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; University of Warwick
- ; The University of Manchester
- University of Nottingham
- ; University of Nottingham
- ; University of Exeter
- ; University of Leeds
- ; University of Oxford
- ; University of Surrey
- University of Cambridge
- ; Cranfield University
- ; Manchester Metropolitan University
- ; Newcastle University
- ; The University of Edinburgh
- ; UWE, Bristol
- ; University of Birmingham
- ; University of Bristol
- ; University of Reading
- ; University of Southampton
- Abertay University
- Imperial College London
- University of Newcastle
- ; Aston University
- ; City St George’s, University of London
- ; Durham University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; King's College London
- ; Loughborough University
- ; Midlands Graduate School Doctoral Training Partnership
- ; Queen Mary University of London
- ; Swansea University
- ; University of East Anglia
- ; University of Greenwich
- ; University of Strathclyde
- ; University of Sussex
- Harper Adams University
- Newcastle University
- University of Liverpool
- University of Oxford
- University of Sheffield
- 31 more »
- « less
-
Field
-
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
-
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
-
to develop forecasting models. The use of machine learning methods for demand modelling could also be considered. The models that are developed will be implemented in a modelling tool which could be used by
-
one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
-
approaches (e.g. SPG) as well as the use of machine learning, advanced computing, statistical modelling to explore the stochastic response to complex scenarios. This project offers the opportunity to undertake
-
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
-
of (or aptitude to learn) quantitative data analysis and coding (e.g. R). Or a background in computer or data science who can demonstrate their ecological or natural history knowledge. Candidates should have a
-
that are not seen in any other material. This project combines cutting-edge sampling techniques with machine-learned potentials for accurate phase predictions, offering considerable opportunity for method
-
, manipulate large datasets, visualise data and perform numerical and statistical analysis is a requirement. Experience in handling 'big data', machine learning and working in distributed teams, is useful
-
Join our diverse and inclusive team to transform the future of aviation as part of the UK’s EPSRC Centre for Doctoral Training in Net Zero Aviation. Offering fully funded, multidisciplinary PhD