Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- ;
- ; The University of Manchester
- University of Nottingham
- ; University of Warwick
- ; City St George’s, University of London
- ; University of Exeter
- ; University of Nottingham
- ; Swansea University
- ; Loughborough University
- ; Newcastle University
- ; University of Bristol
- ; University of Leeds
- ; University of Southampton
- ; University of Surrey
- Abertay University
- Imperial College London
- University of Cambridge
- ; Brunel University London
- ; Coventry University Group
- ; Cranfield University
- ; Durham University
- ; The University of Edinburgh
- ; UWE, Bristol
- ; University of Copenhagen
- ; University of East Anglia
- ; University of Greenwich
- ; University of Strathclyde
- ; University of Sussex
- AALTO UNIVERSITY
- Harper Adams University
- University of Newcastle
- 22 more »
- « less
-
Field
-
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
-
. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of
-
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
-
powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
-
calculations of well-characterized 2D materials, simulations of electron microscopy images, and machine learning methods to reconstruct the 3D atomic positions of materials from a 2D microscopy image. The
-
predict and rationalise XFEL observables are desperately needed such that XFEL results can reach their full potential. Aim This research aims to utilise the latest advances of computational methods (machine
-
, these systems serve as complex functional approximators trained over an input-output data set. ‘Second Wave AI’ is the term used to describe the current glut of 'machine learning' style intelligence, where
-
diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
-
this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling