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Job ID: 144853

Research Associate/Fellow, ACFR – Probabilistic Modelling for Autonomous Decision Making
University of Sydney


Date Posted Jul. 24, 2020
Title Research Associate/Fellow, ACFR – Probabilistic Modelling for Autonomous Decision Making
University University of Sydney
Sydney, New South Wales, Australia
Department Australian Centre for Field Robotics (ACFR)
Application Deadline Aug. 26, 2020
Position Start Date Available immediately
 
 
  • Research Scientist/Associate
  • Industrial Design/Technology
 
 

Research Associate/Fellow, ACFR – Probabilistic Modelling for Autonomous Decision Making

  • The opportunity to develop world class research in integrated, data-driven models of mining fleets, processing plants and the minerals being processed, to facilitate autonomous control and optimisation
  • Located on the Chippendale Campus, Join one of the largest robotics research groups in the world with over 120 researchers and engineers, and 1500m2 of laboratory space
  • Full-time Level A/B, Up to 2 years fixed term with a base salary of $96K p.a. – 128K p.a., plus leave loading and up to 17% superannuation

About the opportunity

The University of Sydney is currently seeking an experienced and self-motivated researcher to investigate and develop machine learning and modelling techniques to support our vision of fully autonomous mining operations within the ACFR’s Rio Tinto Centre for Mine Automation.

The project will involve developing both analytical and predictive modelling capabilities for mining equipment (excavators, trucks, drills, etc), processing plants and material tracking in mining operations. This work is a key requirement of our control and optimisation research programmes, which rely in the ability to understand the state of complex systems and predict the results of specific control and dispatch actions. A key challenge in utilising real-world sensor data from a multitude of operational systems is dealing with uncertainty, conflicting or erroneous data, and enabling humans and/or autonomous systems to understand the state of the equipment, bulk commodity during processing and the state of the mine, despite these challenges. This work is intended to integrate with automation of planning, control and sensing, so a broad understanding of robotics is invaluable.

The role will also involve collaborating with industry to pioneer new capabilities and deploy on real-world systems. This role will provide an exceptional opportunity to work closely with academia and Rio Tinto at the intersection of fundamental research into field-robotics and mine operations. You will work with the Centre’s team of software engineers to facilitate the real-world validation and operational deployment of your academic research. You will be expected to build research areas, engage in academic publication of research, supervise research students, and engage directly with the industry partner. This research-focused role does not require a teaching load, but opportunities may be available if desired.

About you

To be successful you will have a PhD in engineering, computer science, applied mathematics or a related discipline, be a team player with excellent communications skills and satisfy the following key requirements:

  • demonstrated expert knowledge in applied machine learning techniques, information theory and/or Bayesian statistics, supported by relevant publications.
  • demonstrated understanding of general field robotics and automation
  • proficiency with Python (alternately Matlab, C++ or similar)

In addition, the following will be advantageous:

  • experience working in a team-based research or engineering project
  • experience working on industry projects and/or collaboratively with industry partners and domain experts
  • demonstrated expertise using data-derived models for control or optimisation, and an understanding of the challenges of integrating modelling, control, planning and perception
  • work experience in a mining research environment

The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity.

About us

The Rio Tinto Centre for Mine Automation (RTCMA) was established by The Australian Centre for Field Robotics (ACFR) at the University of Sydney in 2007. Since its launch, it has become a world-class research group of scientists and engineers, recognised for both fundamental research and delivering technology to industry. The Centre’s research spans automation of equipment, geological interpretation and optimisation of equipment and processes. Funded by the global mining company Rio Tinto, the aim of the Centre for Mining Automation is to develop and implement the vision of a fully autonomous, remotely operated mine.

Since our inception 160 years ago, the University of Sydney has led to improve the world around us. We believe in education for all and that effective leadership makes lives better. These same values are reflected in our approach to diversity and inclusion and underpin our long-term strategy for growth. We’re Australia’s first university and have an outstanding global reputation for academic and research excellence. Across our campuses, we employ over 7600 academic and non-academic staff who support over 60,000 students.

We are undergoing significant transformative change which brings opportunity for innovation, progressive thinking, breaking with convention, challenging the status quo, and improving the world around us.

How to apply

For more information on the position and University, please view the position description available from the job’s listing on the University of Sydney careers website.

All applications must be submitted via the University of Sydney careers website. Visit sydney.edu.au/recruitment and search by the reference number 864/0720F to apply.

Closing date: 11:30pm, Wednesday 26 August 2020 (Sydney time)

© The University of Sydney

The University reserves the right not to proceed with any appointment.


  • We are committed to diversity and social inclusion. We welcome applications from women (particularly for senior and non-traditional roles), Aboriginal and Torres Strait Islander people, people with a disability, people who identify as LGBTIQ, and people from culturally and linguistically diverse backgrounds.

    © The University of Sydney


 
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