Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- Newcastle University
- University of Nottingham
- University of Sheffield
- ;
- Loughborough University
- University of Cambridge
- ; Swansea University
- ; University of Southampton
- The University of Manchester;
- University of Cambridge;
- University of East Anglia
- University of Exeter;
- University of Greenwich
- University of Newcastle
- University of Oxford
- University of Sheffield;
- 7 more »
- « less
-
Field
-
of quantum sensors for acceleration sensing is a key priority due to its potential to revolutionise inertial navigation, environmental monitoring and geological surveying. Presently, the acceleration sensing
-
This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
-
to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device (SFDI) and also from our custom-built photoplethysmography (PPG) sensor
-
(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
-
research group, which leads pioneering work in multi-sensor navigation, signal processing, and system integrity for aerospace, defence, and autonomous systems. The research will deliver a comprehensive
-
This self-funded PhD research project aims to develop smart sensors based on low-frequency resonance accelerometers for condition monitoring of ultra-speed bearings. The developed smart sensors will
-
microclimates that demand dense sensor networks and reliable data retrieval. This project focuses on developing nature-inspired hardware to deploy Internet of Things (IoT) sensors in forest ecosystems. Combining
-
embedded in soft bodies. These oscillators - recently demonstrated as multifunctional units that can simultaneously act as valves, sensors, and actuators (link ) - self-excite and synchronize without
-
, likely with imaging devices not existing during the development. This will require an approach that considers the physics of the multispectral image formation including the three key variables: sensor
-
language model (LLM) technologies to create advanced, multimodal predictive tools for plant health monitoring. Using imagery from RGB cameras, drones, satellites, and multispectral and hyperspectral sensors