Sort by
Refine Your Search
-
Ecology group, offering modelling expertise, high-performance computing and a flexible, postgraduate-focused research environment ideal for computational-experimental PhD work. Training and Skills You will
-
cities, where benefits are unevenly distributed, and how design or management interventions could enhance resilience and equity. A key component of the research will be developing advanced spatial models
-
expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base of manufacturing research. The Through-life Engineering Services (TES) Centre
-
sanitation industries. Working with our established industry partners, you'll implement your innovations in real operational environments, seeing your research make tangible difference while building
-
trustworthy operation of navigation systems in complex, GNSS-denied scenarios. The ultimate goal is to provide the navigation research community and industry with tools and methods that ensure continuous, high
-
systems and future telecom solutions. This project aims to design a localisation/positioning framework capable of leveraging signals from terrestrial base stations, non-terrestrial networks (presented by
-
the intersection of ecology, machine learning, and sustainable land management, the research will combine field data collection, deep learning model development, and stakeholder co-design to support biodiversity
-
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
-
This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
-
. The primary output will be a validated, open-source detection framework demonstrably meeting enterprise performance benchmarks (e.g., latency, accuracy). The research will contribute new knowledge through