Australia has strong research capabilities, but relative to other OECD countries faces a number of barriers in translating research excellence into innovative products and services . Australia also has a low number of researchers in industry when compared with similar countries , declining rates of STEM education participation , and limited opportunities for industry-focused research training.
This scenario prompted CSIRO, in collaboration with UNSW, to design a new PhD program to deliver work-ready, industry-focussed researchers to bridge the research-industry divide. The first pilot program involves 9 industry partners, who in collaboration with UNSW and CSIRO, offered 11 potential research projects in the areas of data-centric society, manufacturing, materials and energy. The selection process for the pilot program ran between August to November this year, attracting over 100 high quality applicants from diverse backgrounds.
The CSIRO Industry PhD (iPhD) program aims to coordinate and bring together universities, businesses and CSIRO to jointly train new researchers to solve real-world challenges and to generate tangible benefits for industry and the national economy . We seek to help build the ‘next-gen’ workforce – to create research leaders who will understand the needs of industry and enable collaboration across the innovation ecosystem.
Industry PhD projects are closely related to Industry partners’ demands and interests. Each project involves a four-way partnership between the PhD candidate, a business, a university and CSIRO, leveraging the capabilities and experience of each partner. We seek to scale up disruption of the national research training system by involving industry throughout the development and delivery of the PhD program.
PhD candidates will collaborate with their industry partner throughout the program to understand and solve a designated industry challenge, as well as do a six-month internship. They will receive industry-relevant training, including leadership, entrepreneurship and innovation. CSIRO and the universities will provide access to world leading researchers and infrastructure. Candidates’ engagement with industry partners will significantly improve their employability and capacity to contribute to the innovation ecosystem.
The response to the pilot program call for applications exceeded our initial expectations considering it was restricted to domestic applicants only. The successful recruitment campaign demonstrates significant interest in industry-research interaction with applicants declaring that “it is different to every other PhD degree out there, working side by side with industry”, and “this scholarship offers a very unique combination of industry and research experience”.
As we further develop the CSIRO iPhD program model and identify necessary resources, we will look to scale it to include more university and industry partners. We believe that every interaction between the research-industry divide is a critical move towards generational cultural change and securing Australia’s all-important innovation pipeline.
To apply for the CSIRO iPhD program, stay tuned for next rounds to be announced through the program’s web site at http://www.csiro.au/industry-phd. For more information about the program, contact Dr. Fernando Koch (firstname.lastname@example.org), Program Manager.
Some big news from our team
- Dr Chen Cai appointed as Board Director of ITS Australia
- Data61 has a 10-year membership
- Dr Wen Tao 1 of 3 finalists for the Young Professional Award
Data61 Team at the 24th Intelligent Transportation Systems World Congress
24th Intelligent Transportation Systems World Congress
DATA61|CSIRO will be participating at the ITS World Congress in Montreal from October 29th to 2nd of November 2017. Data61 is shaping Australia’s data-driven future and aims to be one of the top leading data-focused research and innovation powerhouse. This year, the Advanced Data Analytics in Transport (ADAIT) group in Data61 will be present at Stand 204 and showcase its capability on: smart transport analytics, traffic simulation and connected vehicle investigations. The team will demonstrate its cloud hosted real-time transport data analytic platform with the following functionalities: data-fusion from multiple real-time transport data sources, monitoring of real-time traffic conditions, visualising mode choice and routing behaviour of public transport users in Sydney, Australia, while also offering the possibility to run macroscopic large scale traffic scenarios for traffic simulation purposes. Connected vehicles are also a focus point for the team which works on analysing GPS positioning accuracy from connected heavy and light vehicles using the DSRC technology. ADAIT will be also hosting 2 special interest sessions on “On-Demand Passenger Transport: Innovative Operation Models” (SIS15) and “Evaluation of Connected and Autonomous Vehicle Trials” (SIS109).
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 email@example.com 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 .
 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 , 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.
 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  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.
 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.
23rd World Congress on Intelligent Transport Systems
DATA61 | CSIRO will be participating at the ITS World Congress in Melbourne from October 10th - 14th, 2016. Data61 is creating our data-driven future. By combining the expertise of the CSIRO Digital Productivity and NICTA teams, we are building the world's leading data focused research and innovation powerhouse. This year, Data61 will present at Stand 3118 its capability on transport analytics, intelligent logistics and asset management. The Advanced Data Analytics in Transport (ADAIT) team will demonstrate its cloud hosted real-time transport data analytic platform. Connected vehicles are also a focus point for the team which is working on analysing GPS positioning accuracy from connected heavy and light vehicles using the DSRC technology. The intelligent logistic team will showcase the innovative solutions to logistics at Port Botany in Sydney. The asset management will demonstrate the structure health monitoring project for Sydney Harbour Bridge. The platform ingests data from multiple real-time transport data sources, monitors real-time traffic condition, and detects real-time traffic anomalies from historical traffic patterns.
Premier's Innovation Initiative - Congestion NSW Govt hires Data61 to ease traffic congestion with big data Data61 out to bust Sydney congestion