Postdoctoral Research Associate/Research Fellow, Mining Operations

Location
Sydney, Australia
Posted
26 Mar 2019
End of advertisement period
25 Apr 2019
Ref
521/0319F
Contract Type
Fixed Term
Hours
Full Time
  • Data Scientist for automation and optimisation of Mining Operations
  • Located at Camperdown in the School of Aerospace, Mechanical and Mechatronic Engineering
  • Fixed term for 1 year with funding secured for a further extension, package, $92,682-$123,682 (dependant on experience) p.a plus leave loading and up to 17% superannuation

About the opportunity 

We are currently seeking a self-motivated and well-qualified data science researcher to contribute to the theoretical and applied tasks of the asset optimisation research group, with a focus on data-driven modelling of equipment and process performance, using machine learning and data analytics techniques. The ability to deploy machine learning techniques will rely on provable performance or operating bounds, which will also form an important part of this research. This 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 be expected to build research areas, engage in academic publication of research, and may also have the opportunity to teach at postgraduate and industry levels.

About you

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

You will have a PhD or close to completion in applied mathematics, engineering, computer science or related discipline, be a team player with good communications skills and satisfy the following key requirements:

  • Demonstrated expertise in probabilistic machine learning, modelling and multi-sensor fusion
  • Experience applying a broad range of machine learning techniques to specific problems, and assessing/comparing suitability of approaches in a given domain/application
  • Experience with validation of specific applications of machine learning for robust, reliable deployment
  • Demonstrated experience in working closely with a software development team to transfer research outcomes into deployable and maintainable software components
  • Demonstrated experience in working collaboratively with application domain experts
  • Desire to work on both theoretical and applied modelling tasks and support validation of the outcomes by experts in the mining industry
  • Experience in software development in Python, Matlab and/or C++

In addition, the following will be advantageous:

  • Experience constructing stochastic models or simulations of complex systems
  • Experience working with uncertain data, or techniques robust to incomplete or incorrect data
  • Work experience in a process engineering or mining research environment

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 candidate information pack 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 521/0319F to apply.

Closing date: 11:30pm 25 April 2019 (Sydney Time)

The University of Sydney is committed to diversity and social inclusion. Applications from people of culturally and linguistically diverse backgrounds; equity target groups including women, people with disabilities, people who identify as LGBTIQ; and people of Aboriginal and Torres Strait Islander descent, are encouraged.

© The University of Sydney

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