Machine Learning Methods for Autonomous Robot Pipe Inspection
|Funding for:||UK Students, EU Students|
|Closes:||8th July 2019|
We wish to invite applications for a PhD Scholarship, commencing in the 2019/20 academic year.
The research aims to develop the next generation of autonomous robots that are able to inspect pipes. Knowledge of Bayesian methods for sensor data fusion, mapping and multiple robot cooperation is expected. A particular focus of the project will be on spatio-temporal methods such as Kriging, known also as Gaussian process regression, Bayesian Convolutional Networks and other methods able to work under uncertainties.
The PhD research is aligned with the EPSRC funded Pipebots platform grant: pipebots.ac.uk.
The developed approaches are going to be tested on the pipe bots whose mission is to detect, communicate and contribute to cleaning the underground pipe network. Different sensor data will be considered such as images (optical, thermal and others), ultrasound and Global Navigation Satellite System (GNSS) data.
The Scholarship is fully funded for 3.5 years for UK/EU Nationals only, covering full tuition fees and offering a tax-free stipend at the EPSRC rate (£15,009 for 2019.20). Full tuition fee funding is only available to UK/EU nationals, non-UK/EU nationals can apply but would only receive a partial contribution towards the tuition fees.
Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology. The successful candidate can have a qualification in mathematics, statistics, physics, aerospace engineering, signal processing, electrical engineering or a related subject. A background in experimental research is essential, as is knowledge of statistical and probabilistic methods. Experience in conducting research projects both individually and as part of a team will be an advantage.
Non UK candidates need to meet the formal English requirements for admission.
How to apply
To apply, please submit a PhD application using the University’s online application system via the Postgraduate online application form link at the following:
Within the application, please state Professor Lyudmila Mihaylova and Dr. Sean Anderson as your preferred supervisors and state the project title as ‘Machine Learning Methods for Autonomous Robot Pipe Inspection ’.
Deadline for applications: Monday July 8, 2019 (9am, BST)
Shortlisted Candidates will be required to attend an interview. The interview will consist of i) a short test of knowledge in machine learning, ii) discussion with the supervisory team.
Interviews are planned to take place after the 10th of July 2019.