3-Year Project, starting 1st April 2017, Scholarship at £14,479 per annum plus tuition fees. Only UK/EU applicants are eligible to apply for the scholarship.
The Surrey Space Centre (SSC) within the Faculty of Engineering and Physical Sciences at the University of Surrey has been at the forefront of research in astronautics for over 35 years. The Surrey Technology for Autonomous systems and Robotics (STAR) Labat SSC currently has a PhD project sponsored by the NDA and managed by the National Nuclear Laboratory (NNL), to investigate and develop machine vision based techniques to automatically detect anomalies within the nuclear plant.
Project Details
Plant inspections of the NDA facilities are a continuous process throughout the lifetime of any plant. This involves detecting anomalies including corrosion, contamination, leaks, spillages and damages (such as cracks and out of specification features like the waste container expansion). This process is normally performed manually that presents safety and cost issues. This project aims to achieve automatic detection and quantification of plant anomalies from video and photographic inspection data either in real-time or during post-processing. The PhD research will address some real and typical challenges of nuclear facilities inspections such as noisy data, low frame rates, radiation speckle interference, poor and varied lighting, low pixel count, lack of color detail from commonly used mono cameras, and the use of legacy analogue cameras. As a result, the project will lead to novel technologies that help augment existing human anomaly detection in real-world nuclear plants.
Conditions:
Applicants should have a good honors Bachelor and/or Masters degree in electrical & electronics engineering, computer science or other relevant scientific subjects. S/he should be able to demonstrate good problem solving and analytical skills as well as knowledge of image processing, machine learning and robotics. Evidence of programming with Matlab and/or C/C++ is essential and experience in machine vision and learning would be a significant advantage.
Application procedures:
Apply to study on the PhD programme at http://www.surrey.ac.uk/postgraduate/electronic-engineering-phd In the application form, clearly state you are applying for the PhD scholarship on Automated Visual Detection of Plant Anomalies supervised by Prof Yang Gao. All applications must be submitted by the 10 November 2016.
Informal requests about the scholarship can be made to Professor Yang Gao at yang.gao@surrey.ac.uk.
Closing Date: 16 Feb 2017
Category: PhD Studentships
