Deep SLAM-Funded PhD opening
Do you want to work for two of the world’s leading research organisations, live in a city with extremely high standard of living, enjoy pristine beaches and sun all year round, and still carry out ground breaking Machine Learning, Robotics research to solve real-world challenges? If so, this position is for you.
The Robotics and Autonomous Systems Group at CSIRO’s Data61 have been developing the state of the art LiDAR-based 3D SLAM systems that are able to be used for driverless navigation, mapping, scene understanding and manipulation of loads and objects. This can be done in industrial, urban or natural environments.
A Fully Funded PhD position is available as a part of a research collaboration between the Robotics and Autonomous Systems group at the Commonwealth Scientific and Industrial Organization (CSIRO) and at either of two high-prestige Queensland Universities, the University of Queensland (UQ) or the Queensland University of Technology (QUT), in Brisbane, Australia. You will receive a scholarship of $27,000 per year for 3.5 years. Top-up of $10,000 per year will be awarded to outstanding students.
Topic: Deep SLAM
Simultaneous Localization and Mapping (SLAM) is a key enabling component of driverless vehicles, robotics and augmented reality. The SLAM goal is to estimate pose of the vehicle and simultaneously generate dense 3D scene reconstruction.
At CSIRO we have developed and deployed state-of-the-art 3D LiDAR-based SLAM systems for the past decade. There is a new direction of research at the intersection of deep learning and geometry based 3D SLAM.
The research in this PhD programme will develop algorithms for geometry-based Deep Learning SLAM in a dynamic and unstructured environment. The PhD programme will involve the development of self or semi-supervised learning methods to address the significant weakness of most current deep networks.
- Must have a Bachelor’s degree with the first Class Honours or a Master’s degree with Research in a relevant area in the past 5 years (e.g., Computer Science, Electrical Engineering, Mechatronics, Physics or other related fields)
- Strong competencies in one or more of the followings areas: Robotics, Computer Vision, Machine learning, Deep Learning.
- Demonstrated strong programming skills in C++ or Python in Linux.
- Demonstrated Research Experience e.g. a good publication record.
- Demonstrated Experience in Robot Operating System, Tensorflow and/or Pytorch.
How to apply
Prospective students should send the following documents in a SINGLE PDF file to Dr. Peyman Moghadam (email@example.com) with the subject [PhD Deep SLAM], including:
- a current c.v.
- details of grades or an academic transcript
- one page cover letter explaining your research background and interests,
Dr. Peyman Moghadam (firstname.lastname@example.org)