[visionlist] [Jobs] Multiple PhD positions in the Deep Learning for Robotics at CSIRO and ANU, Australia
peym.mo at gmail.com
Sun Aug 11 08:21:16 -04 2019
[Jobs] Multiple PhD positions in the Deep Learning for Robotics at CSIRO
and ANU, Australia
Application deadline: *Aug 20, 2019*, Australian time
Multiple PhD positions are available as a part of a research collaboration
between the Robotics and Autonomous Systems group at the Commonwealth
Scientific and Industrial Organization (CSIRO) (in the top 1% of global
research institutions) and the Australian National University (ANU) (Ranked
#1 in Australian University) Australia. You will receive a scholarship of
$28,000 per year (+$10,000 top-up per year, +$5000 conference fund per
year) for 3.5 years all Tax Free! These are ongoing positions to be filled
at any time before the deadline. Students are expected to spend time at
both institutes and publish at the top tier journals and conferences during
*1. Self-Supervised Learning for 3D Multimodal Perception *
Potential impact of deep learning is limited due to the lack of large,
annotated, and high-quality datasets in domains of interest. Annotating
such datasets is laborious, costly and time-consuming. This project
proposes to develop self-supervised learning systems to extract and use the
relevant context given by strong prior spatio-temporal models (e.g. dense
3D reconstructions) as supervisory signals in training. This new concept
will investigate model structures that encodes spatio-temporal data, and
show rapid adaptation of models to new domains (few-shot learning) using
trained embeddings layers (self-supervised, or prior data).
*2. 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.
*3. Hyperspectral Deep Learning*
Hyperspectral cameras are currently undergoing a change from bulky and
expensive equipment towards mobile and portable devices. A hyperspectral
camera comprises of hundreds of bands with shortwave dependencies. Compared
to conventional colour cameras (RGB bands), one could use these shortwave
dependencies to design and develop a deep network for object
classification, semantic segmentation and scene understanding. Both
spectral and spatial relationship needs to be modelled by the deep networks
simultaneously. The research in this PhD programme will develop algorithms
for hyperspectral deep learning. The PhD programme will involve the
development of learning with self-supervision algorithms 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
· 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
*How to apply*
Prospective students should send the following documents in a SINGLE PDF
file to Dr. Peyman Moghadam (peyman.moghadam AT csiro.au) with the subject
[PhD ANU], including:
· a current c.v.
· details of grades or an academic transcript
· one page cover letter explaining your research background and
Adjunct Prof. Peyman Moghadam
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