Sort by
Refine Your Search
-
Employer
- Cranfield University
- University of Nottingham
- ;
- ; Swansea University
- ; University of Exeter
- UNIVERSITY OF VIENNA
- ; Newcastle University
- ; The University of Manchester
- ; University of Birmingham
- ; University of Nottingham
- ; University of Southampton
- Newcastle University
- The University of Manchester;
- ; Brunel University London
- ; King's College London
- ; Loughborough University
- ; St George's, University of London
- ; The University of Edinburgh
- ; University of Bristol
- ; University of Cambridge
- ; University of Plymouth
- ; University of Warwick
- Abertay University
- Coventry University Group;
- Durham University;
- King's College London;
- The University of Edinburgh
- The University of Manchester
- UCL
- University of Birmingham
- University of Cambridge
- University of Exeter
- University of Exeter;
- University of Nottingham;
- University of Sheffield
- 25 more »
- « less
-
Field
-
transition, with an increasing need to deploy large-scale offshore wind turbines in harsh operational environments. As these systems grow in complexity and power rating, ensuring the reliability of generator
-
correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
-
are evaluated in controlled settings and do not fully capture the realities of large-scale ecological applications. This PhD project will investigate Long-Tailed Open-Ended Semantic Segmentation (LTOESS), a
-
of challenges of building large-scale systems. Programming skills in Python. A good Bachelor’s Hons degree (2.1 or above or international equivalent) and/or Master’s degree in a relevant subject (physics
-
materials. The ability of their subcomponents to undergo large amplitude displacement, such as macrocycle shuttling in a rotaxane, make them ideal structures for mechanical coupling. We are currently
-
engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early
-
. Experience in working with large data sets, knowledge of statistics, and some programming expertise is essential. The project is based in ECEHH, at the University of Exeter’s Penryn Campus in Cornwall, and may
-
-developing the principles (e.g. ethics) and methods for anonymising, processing and analysing sensitive data collected by a national charity’s 24/7 helpline for people experiencing or witnessing elder abuse
-
vehicles, data centers, etc.). These devices are mostly power electronic interfaced introducing new types of dynamic phenomena and the need for more detailed models, increasing complexity. In addition
-
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