CSIRO Undergraduate Vacation Scholarships - Data61

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  • Get hands-on research experience
  • Access CSIRO's world-class facilities
  • Apply for a CSIRO Vacation Scholarship today!

CSIRO Undergraduate Vacation Scholarships are run over the Australian summer holidays and offers high achieving and promising undergraduate students the opportunity to collaborate with leading CSIRO scientists in our world class facilities.

Students will have the chance to work on a real project in science, engineering or a related field such as science communication. Placements are full time and will typically begin in late November 2017 or early December and can be from 8 to 12 weeks in length.

Participation in the Vacation Scholarship Program has influenced previous scholarship holders in their choice of further study and future career options. Many have gone on to pursue a PhD in CSIRO or to build a successful research career within CSIRO, a university or industry.

Projects: There are 45 projects available, for details please see the project list

Location: various (please refer to list of projects above for specific details)

Scholarship: $1462.77 per fortnight

Reference: 43691

Pre-Requisites/Eligibility: To be eligible to apply you must be an Australian or New Zealand Citizen, Australian Permanent Resident or an international student who has full work rights for the 8 to 12 weeks duration (does not require visa sponsorship).

Vacation scholarships are open to students who:

  • are currently enrolled in an undergraduate degree at an Australian university;
  • have completed three years of a full-time undergraduate course (exceptional second year
  • students will also be considered);
  • have a strong academic record (credit average or higher); and
  • Intend to go on to honours and/or postgraduate study.

Applications close on: Monday 7 August 2017

Data61 PhD scholarship round for Semester 2, 2017

The Data61 PhD scholarship round for Semester 2, 2017 is open for students enrolled or enrolling in participating universities. Applications are sought from new and existing high quality students working in Data61’s research areas of interest.
Staff interested in engaging PhD students should familiarise themselves with the Scholarship Guidelines and seek out applications from suitably qualified, high achieving students. Applications close 30 April 2017; please see the Scholarship Program website for specific details and contact if you have any questions.

PhD Topics proposed by the ADAIT team in Data61|CSIRO:

"Impact of connected and autonomous vehicles (CAV/AV) on traffic demand and driver behaviour”, Simona Mihaita
In an era of rapid technological changes, connected and autonomous vehicles seem to be the perfect solution for dealing with challenging problems such as congestion, pollution and space optimisation in urban areas. While significant efforts are put together for dealing with regulations, standard adoption and trial cases, few studies have concentrated on studying the impact of adopting such novel technology on the driver behaviour and the actual benefit that it might provide in terms of traffic demand/travel time/congestion management. The main focus is to find innovative research ideas for assessing the benefits of CAV/AV by making use, for example, of current existing CITS platforms in Data61 [1].
[1] Mihaita Adriana Simona, Tyler Paul, Menon Aditya, Wen Tao, Ou Yuming, Cai Chen, Chen Fang, "An investigation of positioning accuracy transmitted by connected heavy vehicles using DSRC", Transportation Research Board 96th Annual Meeting, Washington D.C., January 8-12, 2017.

"Neural networks and deep learning – applications to transport modelling", Young Lee
Research on using neural networks and deep learning in modelling time series has produced mixed results. Whilst neural networks are known to possess great potential due to its ‘universal approximators’ property [1], their ample flexibility can lead to inference issues. Recent papers have attempted to better understand the properties of these models. A good direction in research would be to build on current results by examining some aspects of forecasting using neural networks and deep learning, including the behaviour of time series generated from a model with known parameters. Special attention should be paid to the pitfalls and potential remedies for avoiding them. The applications are enormous, an example could be in the form of time-series approaches for modelling demand for transportation.
[1] Hornik, K.; Stinchcombe, M.; White, H. (1989). Multilayer feedforward networks are universal approximators " Neural Networks. 2(5) pp. 359-366.

"Citywide Crowd Flow Prediction", Weihong Wang
Predicting the flow of crowds in each region of a city is of great importance to traffic management and public safety, and is very challenging due to many complex factors, such as inter-region traffic, events, and weather. Deep-learning-based approaches can be used to forecast the inflow and outflow of crowds in the regions of a city. Specifically, an end-to-end structure of ST-ResNet [1] can be used to work on unique properties of spatio-temporal data and the temporal closeness, period, and trend properties of crowd traffic can be modelled.
[1] Zhang, J.; Zheng Y.; Qi, D. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. AAAI 2017.

"Modelling multi-modal transport systems in transition to Smart Mobility", Le Minh Kieu
New transportation solutions has led to very rapid changes in cities around the world, including the development of shared mobility, mobility-as-a-service and automated vehicles. These new technologies render significant opportunities, but also blurring the distinction between public and private transport, as well as promoting a new mode of transport-the Smart Mobility. It presents substantial challenges in modelling the evolving multimodal transport systems which has not been addressed in literature. This PhD studentship focuses on developing new mathematical models to replicate and predict the dynamics of near-future multimodal transport systems using emerging data sources and advanced machine learning techniques.