Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Exeter
- Cranfield University
- University of Birmingham
- University of Nottingham
- Loughborough University
- University of Cambridge
- University of East Anglia
- University of Sheffield
- ;
- Cranfield University;
- Imperial College London;
- Loughborough University;
- The University of Manchester;
- University of Birmingham;
- University of Cambridge;
- University of Exeter;
- University of Greenwich
- University of Hull;
- University of Oxford
- University of Oxford;
- University of Plymouth
- University of Plymouth;
- University of Sheffield;
- University of Warwick
- 14 more »
- « less
-
Field
-
capable of leveraging signals from terrestrial base stations, non-terrestrial networks such as LEO satellite, and complementary on-board sensors. Specifically, it will: To design reconfigurable airborne
-
the Internet of Things (IoT), where networked sensors and actuators enable real-time adaptation to environmental changes. Consider a self-adaptive IoT network such as a smart home that autonomously manages
-
for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and flight path analysis
-
—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
-
, optoelectronic, neuromorphic; Scale-up and Systems - Nanomanufacturing, cellular manufacturing, sensors /actuators, bioelectronics, IoT, theranostics; Frontiers in Nano-Metrology - in-situ nanometrology, electron
-
operating filters. Quantify operational performance including headloss recovery, filtrate turbidity, biological stability and lifecycle carbon—using high-resolution sensor data and life-cycle assessment tools
-
scintillator-based radiation sensors combining multiple materials with complementary functions, offer a promising route to overcome these limits and achieve unprecedented timing resolution (sub-70ps), enabling
-
to produce anti-counterfeit markings, dye-free colour images, humidity and chemical sensors, anti-glare coatings and optical filters. This project will develop additive manufacturing of devices with actively
-
intensive training in energy modelling, AI-accelerated optimisation, and lifecycle-aware computing. Whether working on smart mobility, sensor nodes, or autonomous platforms, you’ll be contributing to a new