Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- ;
- ; University of Southampton
- University of Nottingham
- ; University of Sheffield
- Cranfield University
- ; Newcastle University
- ; University of Leeds
- ; The University of Manchester
- ; University of East Anglia
- ; The University of Edinburgh
- ; University of Warwick
- Swansea University
- University of Exeter
- ; Swansea University
- ; University of Nottingham
- ; City, University of London
- ; Loughborough University
- ; University of Exeter
- ; University of Surrey
- Newcastle University
- University of Sheffield
- ; Cranfield University
- ; Imperial College London
- ; Northeastern University London
- ; UCL
- ; University of Cambridge
- ; University of Greenwich
- ; University of Hertfordshire
- ; University of Plymouth
- University of Cambridge
- ; Aston University
- ; Babraham Institute
- ; Leeds Beckett University
- ; Midlands Graduate School Doctoral Training Partnership
- ; UWE, Bristol
- ; Universitat Bern
- ; University of Birmingham
- ; University of Bristol
- ; University of Dundee
- ; University of Essex
- ; University of Oxford
- ; University of Sussex
- ; Western University
- Abertay University
- Harper Adams University
- The University of Manchester
- Ulster University
- University of Aberdeen
- University of Hertfordshire
- University of Oxford
- 40 more »
- « less
-
Field
-
Machine Learning techniques to this data to extract the essential information contained within these trajectories. This will be achieved through the following steps: Develop tools to efficiently generate a
-
Project title: Machine Learning models for subgrid scales in turbulent reacting flows Supervisory Team: Temistocle Grenga, Ed Richardson Project description: Supervised deep convolutional neural
-
machine-learning techniques in ST studies. Our approach introduces two innovations: developing sparse Bayesian learning algorithms for efficient small dataset analysis and designing a simulator for
-
networks of these devices we will use digital twins; machine learning models trained to predict physical systems but are differentiable. This project will advance the machine learning methods, particularly
-
Recent years have witnessed significant strides made by machine learning-based computer vision, thus enabling machines to interpret and understand visual information. However, most machine learning
-
developments in machine learning (ML) for phase retrieval. This project is a collaboration with the Ada Lovelace Institute and Diamond Light Source. If you are interested, please contact the supervisor for more
-
related to staff position within a Research Infrastructure? No Offer Description Overview Qualification type: PhD Subject area: Control and Machine Learning Location/Campus: College Lane, Hatfield Closing
-
Supervisory Team: Hector Calvo-Pardo; Vahid Yazdanpanah; Tiago Alves (Solar Americas ); Enrico Gerding PhD Supervisor: Hector Calvo-Pardo Project description: Machine learning (ML) holds immense
-
Overview Qualification type: PhD Subject area: Control and Machine Learning Location/Campus: College Lane, Hatfield Closing application date: 10 June 2024 Start date: July 2024 or as soon as
-
learning called Machine Listening. The PhD project brings together world-leading audio experts to develop state-of-the-art techniques as a widely-applicable embedding of acoustic environments. Based in