Several PhD positions are available to work on a challenging interdisciplinary research project spanning neuroscience, robotics and computer vision. Project summary: Current robotic and personal navigation systems leave much to be desired; GPS only works in open outdoor areas, lasers are expensive and cameras are highly sensitive to changing environmental conditions. In contrast, nature has evolved superb navigation systems. This project will solve the challenging problem of place recognition, a key component of navigation, by modelling the visual recognition skills of humans and the rodent spatial memory system. This approach combines the best understood and most capable components of place recognition in nature to create a whole more capable than its parts. The project will produce advances in robotic and personal navigation technology and lead to breakthroughs in our understanding of the brain. This project is a strongly interdisciplinary one spanning neuroscience, robotics and computer vision and candidates will require experience or a skill set that facilitates this interdisciplinary approach - such as a strong background in computer science with an interest in neuroscience, or a neuroscientist / biologist with strong math and coding skills. PhD candidates will have the very challenging but rewarding task of achieving research breakthroughs that impact multiple disciplines simultaneously, as well as producing innovative technologies based on these breakthroughs. Project Collaborators and Support The project will involve collaboration with leading neuroscientists, roboticists, biologists and computer vision researchers at top international universities including Harvard University and Boston University, as well as local collaborators including The University of Queensland. PhD candidates will pursue an independent interdisciplinary research agenda but will have access to researchers from several major robotics and computer vision initiatives based at QUT, including the Australian Research Council Centre of Excellence in Robotic Vision. How to Apply To apply please go to: https://wiki.qut.edu.au/display/cyphy/Applications+Milford. Scholarships and top-ups are available for high quality applicants. Or contact: Dr Michael Milford: https://wiki.qut.edu.au/display/cyphy/Michael+Milford School of Electrical Engineering and Computer Science Queensland University of Technology Phone: +61 7 3138 9969 | E-mail: michael DOT milford@qut.edu.au Michael Milford |ARC Future Fellow | Microsoft Faculty Fellow | Senior Lecturer | Electrical Engineering and Computer Science Science and Engineering Faculty | Queensland University of Technology S Block, Room S1110, Gardens Point Campus ph 3138 9969 | email michael.milford@qut.edu.au<mailto:c.degroot@qut.edu.au> CRICOS No 00213J * Recent Publications * * Visual Place Recognition Under Extreme Perceptual Change - "SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights", International Conference on Robotics and Automation, link http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6224623 * Navigating without GPS, using only a web camera - "Mapping a Suburb with a Single Camera using a Biologically Inspired SLAM System", IEEE Transactions on Robotics, link http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4627450 * Continual mapping and navigation over a 2 week office delivery robot experiment - "Persistent Navigation and Mapping using a Biologically Inspired SLAM System", International Journal of Robotics Research, http://ijr.sagepub.com/content/29/9/1131.abstract * Combining robotics, neuroscience and animal navigation to manage uncertainty when navigating - "Solving Navigational Uncertainty Using Grid Cells on Robots", PLoS Computational Biology, http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000995 More information available at: https://wiki.qut.edu.au/display/cyphy/Michael+Milford Google Scholar Profile: http://scholar.google.com/citations?user=TDSmCKgAAAAJ&hl=en