Research Fellow in Autonomous Systems in City Infrastructure
Computer Science,Artificial Intelligence,Engineering and Technology,Civil Engineering,Electrical and Electronic Engineering
Short info about job
Company: University of Leeds
Department: School of Civil Engineering / School of Computing
Salary: £32,004 to £38,183 p.a. Grade 7
Hours: Full Time
Contract type: Permanent
Type / Role: Academic or Research
Phone: +44-1550 9840423
Fax: +44-1527 4747985
Detail information about job Research Fellow in Autonomous Systems in City Infrastructure. Terms and conditions vacancy
Location: Leeds - Main Campus
Faculty/Service: Faculty of Engineering
Contract Type: Fixed Term (32 months (external funding))
Interview Date: Tuesday 10 October 2017
Robotics is changing the way we do everything in all walks of life, and Civil Engineering and City Infrastructure is not different. At Leeds we are leading the way in robotic applications to infrastructure and would like now to recruit someone to continue and expand on this area to improve our position internationally. Are you up for the challenge? Do you want to trail blaze the way into the field?
The University of Leeds was awarded a large EPSRC grant to explore the use of robots and autonomous systems in city infrastructure. This 5-year project commenced in January 2016. The team is formed of a consortium of the Universities of Leeds (lead), Birmingham, UCL and Southampton. You will join this team that is already producing outstanding results – e.g. we have produced the first-ever road pavement 3D printer on-board a UAV or developed a robot that can be remotely powered inside a pipe using microwaves.
You will lead the research in the following three areas:
- Use Computer Vision techniques to develop image based sensor systems for visual monitoring of infrastructure defects like cracks in pavements;
- Obtaining 3D reconstruction using techniques such as structure-from-motion (SfM) and/or laser scanning; and
- Develop techniques to identify objects, parts of objects, semantic scene regions and activities in order to facilitate navigation and task performance by lightweight UAVs; it is expected that this will include the use of qualitative spatio-temporal representations.
Holding a PhD (or close to completion) in Computer Vision or a closely allied discipline, you will have knowledge of machine learning and deep learning techniques and an enthusiastic, creative approach to research.
To explore the post further or for any queries you may have, please contact:
Raul Fuentes, Pro-Dean International and Associate Professor of Infrastructure Engineering, School of Civil Engineering, Tel: +44 (0)113 343 2282 or email: [email protected]
Anthony Cohn, Professor of Automated Reasoning, School of Computing, Tel +44 (0)11334 35482 or email [email protected]
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