Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of East Anglia
- Imperial College London;
- The University of Manchester
- University of Exeter;
- University of Nottingham
- Loughborough University
- University of Birmingham;
- University of Cambridge;
- University of Exeter
- University of Sheffield
- AALTO UNIVERSITY
- Bangor University
- KINGS COLLEGE LONDON
- The University of Edinburgh;
- The University of Manchester;
- University of Birmingham
- University of Cambridge
- University of East Anglia;
- University of Warwick
- ;
- Edinburgh Napier University;
- Loughborough University;
- Oxford Brookes University
- University of Bristol
- University of Nottingham;
- University of Oxford;
- University of Sheffield;
- University of Surrey
- ; University of Exeter
- European Magnetism Association EMA
- King's College London
- King's College London;
- Liverpool John Moores University
- Manchester Metropolitan University;
- Newcastle University
- Swansea University;
- The University of Edinburgh
- UCL
- Ulster University
- University of Bradford;
- University of Essex
- University of Hull;
- University of Leeds
- University of Liverpool
- University of Liverpool;
- University of Oxford
- University of Plymouth
- University of Warwick;
- University of York;
- 40 more »
- « less
-
Field
-
Research theme: "Next Generation Wireless Networks", "Signal Processing", "Machine Learning" UK only How to apply: uom.link/pgr-apply-2425 This PhD project aims to design novel resource allocation
-
both sites. The project sits at the interface of cell line engineering, protein science and machine learning and you will receive advanced training in these areas while developing methods to accelerate
-
filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
-
synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
-
This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
-
Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment
-
, machine-learning tools, and Lagrangian transport modelling. You will be based at the British Antarctic Survey and work closely with experts at the University of Leeds and Exeter, who provide cutting-edge
-
will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
-
contribute significantly to these growing fields. This PhD position is ideal for candidates interested in the following areas of machine learning: Geometric learning: exploiting the structure of data (e.g
-
-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep