[robotics-worldwide] [jobs] Postdoctoral researchers in optimisation and scheduling for autonomous mining at RTCMA/ACFR, University of Sydney, Australia

Andrew Hill andrew.hill at sydney.edu.au
Sun Mar 31 19:31:39 PDT 2019


The Rio Tinto Centre for Mine Automation, a group within the Australian Centre for Field Robotics at the University of Sydney have several research-focussed postdoctoral positions open.

Since 2007, the Centre’s focus has been on applying fundamental research to real-world problems in the mining domain. We have a strong and long-standing relationship with Rio Tinto and a record of successful translation of research into industry. The Centre is currently funded through to 2021. As an applied-research centre, we have a mix of academic researchers and software engineers to facilitate translation of ideas into production systems. Further software development positions will be forthcoming.

Stochastic fleet optimisation researcher (447/0319F)<https://urldefense.proofpoint.com/v2/url?u=https-3A__sydney.nga.net.au_-3Fjati-3DE6F72325-2D3785-2DC188-2DE795-2DAD19BFB832B5&d=DwIGaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=evERSwYIUB1diR1Mb50rOVnraUFVoTP49A_2GQrqB-E&s=j3Ak_BUo6FevKkTG0VRtHjk2iQE-6qsHT_1_cxoSZ-w&e=>

We are currently seeking a self-motivated and well-qualified researcher to contribute to theoretical and applied optimisation research, with a focus on optimisation of mining equipment and agents across complex systems. The work will include data-driven modelling of equipment and process performance. 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.

Applications and more information: https://urldefense.proofpoint.com/v2/url?u=https-3A__sydney.nga.net.au_-3Fjati-3DE6F72325-2D3785-2DC188-2DE795-2DAD19BFB832B5&d=DwIGaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=evERSwYIUB1diR1Mb50rOVnraUFVoTP49A_2GQrqB-E&s=j3Ak_BUo6FevKkTG0VRtHjk2iQE-6qsHT_1_cxoSZ-w&e=

Data science researcher (521/0319F)<https://urldefense.proofpoint.com/v2/url?u=https-3A__sydney.nga.net.au_-3Fjati-3D54A4C7CD-2DF044-2DD139-2D848C-2DAD19BFA04C75&d=DwIGaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=evERSwYIUB1diR1Mb50rOVnraUFVoTP49A_2GQrqB-E&s=4U4cXYo6Q6M4Hapz_0up12s-iVaWmrsrjJP9Tn7rPgs&e=>

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.

Applications and more information: https://urldefense.proofpoint.com/v2/url?u=https-3A__sydney.nga.net.au_-3Fjati-3D54A4C7CD-2DF044-2DD139-2D848C-2DAD19BFA04C75&d=DwIGaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=evERSwYIUB1diR1Mb50rOVnraUFVoTP49A_2GQrqB-E&s=4U4cXYo6Q6M4Hapz_0up12s-iVaWmrsrjJP9Tn7rPgs&e=


Dr Andrew Hill | Research Lead
Rio Tinto Centre for Mine Automation
Australian Centre for Field Robotics
Engineering & IT | The University of Sydney
Rm 309, Sydney Robotics Hub J18
8 Little Queen St, Chippendale NSW 2008
T +61 2 9351 4209 | M +61 421 043 764
acfr.usyd.edu.au<https://urldefense.proofpoint.com/v2/url?u=http-3A__sydney.edu.au&d=DwIGaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=evERSwYIUB1diR1Mb50rOVnraUFVoTP49A_2GQrqB-E&s=fRyFCGLyh62i9fgs8cKE1zhSW1sUv8iqJrPHNUQa7x4&e=>
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