[robotics-worldwide] PhD opening at UTS (Sydney) in Robotics, Machine learning and Physics/Signal Processing
Teresa Vidal Calleja
Teresa.VidalCalleja at uts.edu.au
Thu Nov 15 22:20:58 PST 2012
**PhD opening at UTS (Sydney) in Robotics, Machine learning and Physics/Signal Processing**
The Centre for Intelligent Mechatronic Systems at the University of Technology Sydney (UTS) is looking for a highly motivated PhD student to join a large industry-driven multidisciplinary research project. The industrial partners of the project include key national/international water utilities and providers of infrastructure monitoring technologies (see below for a brief description of the project). The post is available from January 2013. The studentship will cover fees and a tax-free stipend of around AU$24,000 per annum. Research program will be supervised by A/Prof Jaime Valls Miro and Dr Teresa Vidal-Calleja.
The successful candidate must have a degree(s) in Engineering, Physics, Maths, Computer Science or related field, with a solid background in one or more of the following domains: machine learning, robotics and computer vision.
Applicants should submit a cover letter addressing the selection criteria below, curriculum vitae (including publications), and contact information of at least two referees. Applications should be sent to Teresa Vidal-Calleja (Teresa.VidalCalleja at uts.edu.au) in a single pdf document. Applications can be sent immediately and will be evaluated until the 20th December 2012.
• Excellent C/C++ and Matlab programming skills.
• Experience in machine learning methods.
• Experience in simultaneous localisation and mapping (SLAM).
• Experience in Bayesian classification/regression.
• Experience in pattern recognition/signal processing.
Project Title: Automatic Data Interpretation and Enhanced Localisation for In-line Remote Field Eddy Current Tools
Brief description: Remote Field Eddy Current (RFEC) technology allows in situ inspection of metallic water distribution pipes. RFEC tools provide the location and magnitude of defects on the inspected pipes and construction features such as joints, elbow and off-takes. The main goal of this project is to investigate the application of machine learning algorithms for the automatic detection and classification of defects and construction features in the RFEC signals. Moreover, accurate tool localisation is essential to produce a correct detection of the defects and features, this project will also aim at methodologies to enhance the localisation of the in-line RFEC signals using sensors and algorithms commonly used in robotics.
Dr Teresa A. Vidal-Calleja
Centre for Autonomous Systems (CAS)
FEIT, University of Technology Sydney
PO BOX 123, Broadway NSW 2007, Australia
Teresa.VidalCalleja at uts.edu.au
P +61 2 9514 2676
F +61 2 9514 2655
UTS CRICOS Provider Code: 00099F
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