Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- University of Nottingham
- ; Swansea University
- ; University of Nottingham
- ; The University of Manchester
- University of Sheffield
- University of Cambridge
- ; University of Reading
- ; City St George’s, University of London
- ; University of Birmingham
- ; University of Exeter
- ; University of Surrey
- ; University of Warwick
- Imperial College London
- ; Newcastle University
- ; University of Leeds
- ; Cranfield University
- ; The University of Edinburgh
- ; University of Oxford
- ; University of Sussex
- University of Newcastle
- University of Oxford
- ; Loughborough University
- ; University of Bristol
- ; University of Southampton
- Abertay University
- THE HONG KONG POLYTECHNIC UNIVERSITY
- UNIVERSITY OF VIENNA
- University of Manchester
- ; Aston University
- ; Brunel University London
- ; Coventry University Group
- ; Durham University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Edge Hill University
- ; Imperial College London
- ; Royal Northern College of Music
- ; UCL
- ; UWE, Bristol
- ; University of East Anglia
- ; University of Greenwich
- ; University of Hertfordshire
- ; University of Sheffield
- ; University of Strathclyde
- AALTO UNIVERSITY
- Harper Adams University
- Heriot Watt University
- KINGS COLLEGE LONDON
- University of East London
- University of Liverpool
- 41 more »
- « less
-
Field
-
for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
-
This large-scale ecological project investigates the barriers and drivers of post-fire forest recovery. With climate change and the spread of forest fires to new areas, it is important to investigate the conditions that support forest recovery after a fire. The study areas can be defined using...
-
optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
-
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
-
the accurate prediction of reaction enthalpies and activation free energies for all relevant intermediates. In this project, a deep learning and generative design toolchain will be developed resulting in an ML
-
of dehydration using a low-power radio-frequency (RF) sensor. The research objectives include design optimization to improve wearability, robust data acquisition using machine learning and establishing correlation
-
this research is that it should be possible to significantly improve the performance of extreme learning and assure safe and reliable maintenance operation by integrating this prior knowledge into the learning
-
and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies
-
Characterisation" "Data Science and Machine Learning in Materials" "Plastics Recycling and Circular Economy" Research theme: "Materials Characterisation" "Data Science and Machine Learning in Materials" "Plastics
-
on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine