Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- ;
- University of Sheffield
- Cranfield University
- Heriot Watt University
- Imperial College London
- University of Glasgow
- AALTO UNIVERSITY
- Durham University
- Loughborough University
- University of Bristol
- ; The University of Manchester
- ; University of Sheffield
- DURHAM UNIVERSITY
- Glyndwr University
- Oxford Brookes University
- UNIVERSITY OF SURREY
- Ulster University
- University of Birmingham
- University of Newcastle
- University of Oxford
- University of Stirling
- 11 more »
- « less
-
Field
-
control laws into Trent gas turbine engines and developed algorithms monitoring fleets of 100s of engines flying all around the world. During the PhD, you will have the opportunity to deeply engage with
-
of fingers, the shapes of the fingers, and the positions of tactile sensors), and the control policy for that hand, when given a particular task or set of tasks. Through this, we aim to develop a framework
-
systems with a particular emphasis on methods and systems that cope with imperfect knowledge and uncertain sensors. The research environment provides excellent opportunities for open-minded co-operation
-
engage in immersive, simulated construction tasks, while wearable sensors monitor their physical effort, emotional states, and cognitive load. Physiological and behavioural data — including eye tracking
-
consists of three major parts: sensor printing, circuit design and integration and developing of an AI algorithm and using it to teach the sensor to selectively measure desired gases . In this role, you will
-
The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
-
collaboration between the OU and Teledyne e2v (T-e2v), a world-leading manufacturer of scientific and industrial image sensors. The CEI is dedicated to conducting research into advanced imaging technologies
-
quality, and real-time AI performance. This research hub, tackles the intricate challenges of cyber-disturbances and data quality in Edge Computing (EC) environments supporting AI algorithms. The role
-
-identified scans, records and sensor feeds to answer questions such as: Can we predict a patient’s response to treatment without ever seeing their raw file? Can an algorithm learn the warning signs of trouble
-
of the fingers, and the positions of tactile sensors), and the control policy for that hand, when given a particular task or set of tasks. Through this, we aim to develop a framework that can automatically