The Robotics and Autonomous Systems Group at CSIRO’s Data61 have been developing algorithms for Robotics and Autonomous systems for the past 2 decades. We deploy our systems to real-world applications such as agriculture, emergency responders and mining.
There is a new direction of research at the intersection of deep learning and Robotics. The research will develop algorithms for geometry-based Deep Learning algorithms in a dynamic and unstructured environment. The research will involve the development of self or semi-supervised learning methods to address the significant weakness of most current deep networks, lack of labelled data. We will also investigate introduction of Bayesian deep learning to understand and model uncertainty to improve safety for real world applications.
We need motivated students to work on all aspects of applying Deep Learning to real-world field robots.
This is an opportunity for Masters and Phd students required to complete a placement or Thesis Project as a component of their tertiary study to immerse themselves in an innovative environment to gain valuable skills and experience that you won’t find anywhere else.
The duration of this placement is a minimum of 8 months in duration and it will provide you with access to our leading scientists and mentors, real-life experience working in the robotics area while completing a component of your tertiary study.
Local undergraduate students (UQ or QUT) are eligible to apply for Honour Thesis Projects. The duration is for 12 months (2 semesters).
We expect to see a record of a satisfactory academic standard, and if applications are from overseas, be assured that students have sufficient funds to cover their travel and living costs whilst in Australia. It is recommended that international applicants view the Australian Government website ‘Study in Australia’.
Required skills: Programming (C++, Python), strong in Math, Good knowledge of Machine Learning and Robotics.
Industrial internships are not part of a formal program so to apply please contact Adjunct Associate Professor Peyman Moghadam via email@example.com to discuss your interest.
Please supply the following information and documentation in a SINGLE PDF FILE at time of contact for your application to be considered:
Adjunct Associate Professor Peyman Moghadam via firstname.lastname@example.org