In this project the PhD research student needs to understand the strengths and weaknesses of the deep convolutional neural network for analysis of civil infrastructures, such as concrete bridges, highways, buildings, tunnels and stadiums, using videos/images captured by a network of multiple cameras. The student research work will be on finding contextual information for reidentification of the civil structural components (CSCs), such as columns, beams, slabs, arches, plates, shells, etc, and a procedure to incorporate them in the network consistent data association problem in a network of cameras. This will be followed by analysis of unhealthy (defects such as spallation, exposed bar, corrosion, crack, etc) areas in the CSCs. The student needs to study, analyse and conduct experiments systematically to model and design new semi-supervised and/or unsupervised deep networks that outperforms the current-state-of-the-art algorithms on standard benchmarking and real-world databases.
These are active and important areas of research with many opportunities for innovation and collaboration. This project will provide an opportunity to pursue world-class research environment, provide experience of design and evaluation processes and an opportunity for substantial contribution to international publication in leading journals/conference proceedings.
Keele University is renowned for its exciting approach to higher education and research,beautiful campus, strong community spirit and excellent student life. The University has the UK’slargest campus with 617 acres of landscaped parkland, fields, woodlands and lakes. KeeleUniversity runs its own day nursery for infants from 3 months to 5 years and is committed to
equality and diversity. Information for prospective postgraduate researchers can be found here: http://www.keele.ac.uk/pgresearch/
Research Context: This PhD project will be a part of visual inspection of civilinfrastructures collaborative research inspired by the challenging problems faced by visual inspectors in civil, structural and earthquake engineering. The research will be supervised by Dr Bappaditya Mandal in the Centre for Computer Science Research at Keele University and industry collaboration with Arcadis, Birmingham, UK and potentially, with other national and international project partners.
Applications are welcomed from science, technology, engineering or mathematics graduates with (or anticipating) at least a 2.1 honours degree or equivalent. Applicants should have good computing skills and an enthusiasm for designing and testing new algorithms. They should be self-motivated and have the ability to work both independently and as part of a team.
This opportunity is open to UK/EU and overseas students. The collaborative and presentation aspects of the research require good English language and communication skills. Overseas applicants would therefore require an English IELTS (or equivalent) of 6.0 overall with no less than 5.5 in any subtest.
There are 2 - 3 studentships available. Please quote FNS GS 2019-29 on your application.
Applications are welcomed from science, technology, engineering or mathematics graduates with (oranticipating) at least a 2.1 honours degree or equivalent. Applicantswill require good general programming skills but will not need specific computing expertise in, for example, Computer Vision,Machine Learning and Video Analytics. Applicants should have an enthusiasm for design and experimentation as well as awillingness to acquire new skills.Ideally, applicants will be self- motivated and have the ability to work both independently and as part of a team.
This opportunity is open to UK/EUand overseas students. The collaborative and presentation aspects of the research require good English language and communication skills. Overseas applicants would therefore require an English IELTS (or equivalent) of 6.0 overall with no less than 5.5 in any subtest.
Attitude and Personality
Applicants should be self-motivated and enjoy working both independently and as part of a team.
Informal enquiries about the project are very welcome by email to the Project Lead, Dr Bappaditya Mandal, [email protected]
. Full applications should be submitted to: .https://www.keele.ac.uk/study/postgraduateresearch/researchareas/computerscience/
Please quote FNS GS 2019-29 on your application.
Keele University values diversity, and is committed to ensuring equality of opportunity. In support of these commitments, Keele University particularly welcomes applications from women and from individuals of black and ethnic minority backgrounds for this post. The School of
Computing and Mathematics and Keele University have both been awarded Athena Swan awards and Keele University is a member of the Disability Confident scheme. More information is available on these web pages: https://www.keele.ac.uk/equalitydiversity/ https://www.keele.ac.uk/athenaswan/ https://www.keele.ac.uk/raceequalitycharter/raceequalitycharter/