Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of East Anglia
- University of Nottingham
- ; City St George’s, University of London
- AALTO UNIVERSITY
- University of Sheffield
- ;
- ; Swansea University
- ; The University of Manchester
- ; University of Exeter
- ; University of Nottingham
- The University of Manchester
- University of Cambridge
- University of Cambridge;
- Bangor University
- KINGS COLLEGE LONDON
- The University of Manchester;
- University of Bristol
- University of Sheffield;
- University of Surrey
- ; Brunel University London
- ; Coventry University Group
- ; Newcastle University
- ; UCL
- ; University of Leeds
- ; University of Southampton
- ; 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 Birmingham;
- University of Exeter
- University of Liverpool
- University of Newcastle
- University of Nottingham;
- University of Oxford
- University of Warwick
- 34 more »
- « less
-
Field
-
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
-
for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
-
Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
-
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
-
for their employability in applications. Additionally, machine learning methods need to be applicable to high-dimensional and to noisy data that are typically encountered in real-world applications. The aim of this project
-
ecosystem services such as carbon storage (1-4). Recent advances in satellite observations and machine learning provide novel opportunities to study extreme fires on a global scale. In a changing climate
-
data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
-
? This PhD project offers a unique opportunity to apply machine learning to solve a critical engineering challenge within the railway industry. The Challenge: Rail grinding is a crucial maintenance activity