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Main SupervisorPosition Details and Title/topicShort Description

Associate Professor Michael Milford

 

1 postdoctoral research fellow

Robotics, machine learning, computational neuroscience, deep learning

Term: 6 months to 3 years

This project combines modelling of the spatial memory encoding system in the mammalian brain with machine learning techniques to investigate new compression techniques for encoding and recalling information that scale well to very large datasets.

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. Postdocs will have the very challenging but rewarding task of achieving research breakthroughs that impact multiple disciplines simultaneously.

Funded by the Asian Office of Aerospace Research & Development

Skills and experience required:

  • High level of competency in coding in C++, Python or Matlab (ideally more than one)
  • High level of competency in using robotic software and libraries such as ROS and computer vision libraries such as OpenCV
  • High level experience in at least one of (ideally both) a) robotic navigation / SLAM / perception and b) computational neuroscience
  • Experience leading the writing of peer-reviewed academic papers that have been published in top tier international conferences and journals in at least one of the below fields:
    • In robotics, conferences: IROS, ICRA, RSS, FSR journals: IEEETRO, IJRR, JFR
    • In computer vision, conferences: CVPR, ICCV, ECCV, WACV, journals: PAMI, IJCV
    • In neuroscience / computational neuroscience, J Neuro, PLoS CB, Hippocampus, Neural Networks
  • The ability to rapidly read and distill key ideas and concepts from large quantities of literature across multiple fields

Enquiries: michael.milford@qut.edu.au

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Associate Professor Michael Milford

1 postdoctoral research fellow

Robotics, computational neuroscience, place recognition, navigation

Term: 6 months to 2 years

This project will revolutionize our understanding of how humans and animals use vision to determine their location in the world. This understanding will lead to new computer algorithms that enable robots to navigate in any environmental conditions using cheap visual sensors and breakthroughs in our knowledge 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. Postdocs will have the very challenging but rewarding task of achieving research breakthroughs that impact multiple disciplines simultaneously.

Funded by the Australian Research Council

Skills and experience required:

  • High level of competency in coding in C++, Python or Matlab (ideally more than one)
  • High level of competency in using robotic software and libraries such as ROS and computer vision libraries such as OpenCV
  • High level experience and published outputs in at least one of (ideally all) a) robotic navigation / SLAM / vision b) computational neuroscience and c) deep learning / machine learning
  • Experience leading the writing of peer-reviewed academic papers that have been published in top tier international conferences and journals in at least one of the below fields:
    • In robotics, conferences: IROS, ICRA, RSS, FSR journals: IEEETRO, IJRR, JFR
    • In computer vision, conferences: CVPR, ICCV, ECCV, WACV, journals: PAMI, IJCV
    • In neuroscience / computational neuroscience, J Neuro, PLoS CB, Hippocampus, Neural Networks
  • The ability to rapidly read and distill key ideas and concepts from large quantities of literature across multiple fields

Enquiries: michael.milford@qut.edu.au


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Associate Professor Michael Milford

1 postdoctoral research fellow

Robotic vision, robust sensing, machine and deep learning

Term: Up to 3 years

Funding is available for a research postdoc at the Australian Centre for Robotic Vision, working on the Robust Vision program under Associate Professor Michael Milford and with collaboration across other nodes in the centre including ANU, Adelaide Uni and Monash Uni.

We are especially looking for candidates with experience and interest in robust robotic vision and perception under challenging conditions such as low light and all weather illumination, and those with experience developing and applying innovative sensing technologies including event cameras and low light cameras.

Funded by the Australian Research Council

Skills and experience required:

    • High level of competency in coding in C++, Python or Matlab (ideally more than one)
    • High level of competency in using robotic software and libraries such as ROS and computer vision libraries such as OpenCV
    • High level experience and published outputs in at least one of (ideally all) a) robotic navigation / SLAM / computer vision and b) deep learning / machine learning
    • Experience leading the writing of peer-reviewed academic papers that have been published in top tier international conferences and journals in at least one of the below fields:
      • In robotics, conferences: IROS, ICRA, RSS, FSR journals: IEEETRO, IJRR, JFR
      • In computer vision, conferences: CVPR, ICCV, ECCV, WACV, journals: PAMI, IJCV
    • Experience working with innovative vision hardware such as event cameras (DVS, Davis), conventional low light cameras, fish eye and panoramic optics
    • The ability to rapidly read and distill key ideas and concepts from large quantities of literature

Enquiries: michael.milford@qut.edu.au


   

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Associate Professor Michael Milford

 

Dr Thierry Peynot

2 x research fellows

Robotics, computer vision, engineering

Term: 2 years

Reliably estimating the position of mining vehicles is the most urgently requested innovation for improving productivity and safety for the underground mining industry in Queensland and worldwide. Current underground positioning technologies require expensive, bulky sensing equipment and regularly fail in a number of scenarios: when the environment changes (as mines do), when the tunnel geometry is particularly uniform, and when adverse environmental conditions are encountered such as airborne dust. This project will build on QUT’s world leading research in camera- and multi-sensor-based navigation algorithms to develop an innovative and disruptive positioning system for underground mining.

Funded by the Queensland Government, Caterpillar and CRCMining (now Mining3).

Skills and experience required:

    • High level of competency in coding in C++, Python or Matlab (ideally more than one)
    • High level of competency in using robotic software and libraries such as ROS and computer vision libraries such as OpenCV
    • High level experience and published outputs in at least one of (ideally all) a) robotic navigation / SLAM / computer vision and b) deep learning / machine learning
    • Experience leading the writing of peer-reviewed academic papers that have been published in top tier international conferences and journals in at least one of the below fields:
      • In robotics, conferences: IROS, ICRA, RSS, FSR journals: IEEETRO, IJRR, JFR
      • In computer vision, conferences: CVPR, ICCV, ECCV, WACV, journals: PAMI, IJCV
    • Experience in a high pressure environment driven by deadlines around demonstrations for stakeholders and publication deadlines
    • Experience working with robots or vehicles in the field and debugging sensors and algorithms in the field

Enquiries: michael.milford@qut.edu.au, t.peynot@qut.edu.au

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