Researcher position in machine learning at NICTA, Australia

[Yanchang Zhao]

NICTA (National ICT Australia Ltd) is Australia’s Information and
Communications Technology Research Centre of Excellence.

Applications are invited for a researcher position in machine
learning, with a particular emphasis on learning dynamic traffic
networks. The researcher will be designing and developing machine
learning models and algorithms that assist building intelligent
transportation systems (ITS) for solving real world traffic problems.
This position is also a part of significant research projects in
NICTA's infrastructure, transport and logistics business theme.

Your primary responsibility will be to build your research reputation
by publishing high quality papers in world-leading venues, while
participating to the design and implementation of
machine-learning-based ITS models, with the aim of improving road
traffic efficiency, providing decision support, forecasting traffic
patterns, ensuring road safety etc. Methods used will include, but not
limited to, graph theory, matrix/tensor factorisation, collaborative
filtering, conditional random fields, Bayesian networks, causal
inference, anomaly detection.  You will be expected to work as part of
an inter-disciplinary team and to collaborate with university
researchers as well as external groups. Other key responsibilities
will be to provide and contribute to the emerging machine learning for
ITS research projects in NICTA. Collaboration with industry project
partners as appropriate and within the scope of activities of the
NICTA Australian Technology Park Laboratory will be involved.

To be successful in this role you will hold a PhD in machine learning,
data mining, statistics or other related areas. Applicants with
experience in working with time-evolving graphs/networks, or other
spatial-temporal data, will be preferred.

Closing date: 17 February 2013

To apply for this role please visit
https://nicta.silkroad.com/epostings/index.cfm?fuseaction=app.jobinfo&id=23&jobid=145&company_id=16182&version=1&source=ONLINE&JobOwner=993272&level=levelid1&levelid1=2770&parent=Research&startflag=2