Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Loughborough University
- University of Nottingham
- Cranfield University
- The University of Edinburgh
- Royal College of Art
- University of Sheffield
- ;
- ; The University of Edinburgh
- Liverpool John Moores University
- NORTHUMBRIA UNIVERSITY
- Newcastle University
- Oxford Brookes University
- Queen Mary University of London
- The University of Manchester
- The University of Manchester;
- UNIVERSITY OF SURREY
- University of Birmingham;
- University of Cambridge;
- University of East Anglia
- University of Exeter;
- University of Oxford
- University of Strathclyde;
- University of Warwick
- 13 more »
- « less
-
Field
-
AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
-
of mission design, manufacturing, operations, and disposal. This PhD project aims to advance sustainable space operations by developing a holistic lifecycle assessment framework and computational tool
-
electronics, enabling spontaneous gaits powered by a single onboard pressure source. The project’s vision is to establish embodied oscillator intelligence, where locomotion arises from the physics of coupled
-
Students Project Description The NetZero Futures (NZF) Doctoral Landscape Award is a fully funded EPSRC studentship with the Royal College of Art. The strategic vision of NZF unites RCA-wide Art and Design
-
research frontier in computer vision that combines three critical challenges: class imbalance, recognition of rare and unseen species, and dense labelling of high-resolution imagery. The candidate will
-
One fully funded, full-time PhD position to work with Alessandro Suglia in the Embodied, Situated, and Grounded Intelligence (ESGI) group at the School of Informatics, University of Edinburgh
-
This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
-
collection activities. Supervision will be provided by academics from various disciplines specializing in biomechanics, image processing, and computer vision, alongside orthopaedic surgeons and academics.
-
the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
-
the University of Nottingham, contributing to cutting edge research into clean and sustainable energy technologies. Vision and Aim Ammonia is an essential component of the global strategy to achieve net-zero