Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- Loughborough University
- University of Sheffield
- University of East Anglia
- University of Nottingham
- ;
- ; Coventry University Group
- ; University of Exeter
- Abertay University
- Coventry University Group;
- Manchester Metropolitan University;
- Oxford Brookes University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Birmingham
- University of Birmingham;
- University of Cambridge
- University of East Anglia;
- University of Exeter
- University of Newcastle
- University of Warwick
- University of Warwick;
- 11 more »
- « less
-
Field
-
by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
-
. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
-
This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
-
, combining both accuracy and explainability; (3) extend statistical learning theory to offer theoretical bounds for intrinsically-aligned AI models; (4) employ the newly-developed metrics to train deep neural
-
analysis will focus on building sophisticated Deep Learning models, e.g., Long Short-Term Memory (LSTM) networks, to accurately model DPs over time and predict mood deterioration. The project will implement
-
experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Direct, hands-on experience working with Large Language Models (LLMs) and/or transformer models. Familiarity and experience working with
-
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
-
At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and transferable skills to tackle tomorrow’s challenges. SIT collaborates with industry in
-
develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
-
into the generation process. This multidisciplinary project will deliver deployable models, reproducible methods, and, where allowed, shareable datasets. The student will gain training in deep learning, AI, image