Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ; The University of Manchester
- University of Nottingham
- ;
- Cranfield University
- ; University of Nottingham
- ; City St George’s, University of London
- ; University of Southampton
- ; Brunel University London
- ; Coventry University Group
- ; Durham University
- ; Newcastle University
- ; The University of Edinburgh
- ; UCL
- ; University of Bristol
- ; University of Cambridge
- ; University of Greenwich
- ; University of Leeds
- ; University of Sussex
- ; University of Warwick
- AALTO UNIVERSITY
- Abertay University
- Harper Adams University
- University of Cambridge
- 13 more »
- « less
-
Field
-
current best-in-class benchmarks. Beyond the automotive sector, the project might also contribute to our understanding of how other spaces can be designed to support the required user experiences
-
at the intersection of machine learning, bioinformatics, and computational pathology. Project Overview: Integrating histopathological imaging with omics (e.g., transcriptomics, genomics, proteomics) holds tremendous
-
Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Research theme: "Materials Characterisation" "Data Science and Machine Learning in
-
robotics, and materials science. Project description: 3D-printing of soft robotics is a growing field, with many applications in biomedical devices, electronics, and autonomous machines. Actuators to drive
-
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
-
representations are developed. In particular, the project will support designers to achieve driver and passenger experiences that surpass current best-in-class benchmarks. Beyond the automotive sector, the project
-
datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
-
for innovative solutions to improve worker well-being. The project proposes a novel, integrated framework leveraging virtual reality (VR), the internet of things (IoT), and machine learning (ML). Workers will
-
, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Strong background/skills on machine learning, mathematics, probabilistic
-
solutions that extend the operational life of devices and reduce environmental impact, applicable to areas like smart grids, electric vehicles, and portable electronics. Research Focus Areas: Power-Aware