Excellent opportunity to join Swinburne’s Department of Computer Science and Software Engineering and collaborate across multiple disciplines
About the Job
We are seeking a Research Fellow to undertake research in “Data-Driven Traffic Analytics and Simulation for Incident Analysis and Management”, supported by the ARC Linkage Funding Scheme. The Fellow will be positioned in the Department of Computer Science and Software Engineering located at the Hawthorn Campus, and work closely with Swinburne researchers in intelligent transport systems from the Department of Civil and Construction Engineering and also the external researchers from Data61, UTS, NTU (Singapore) and NUS (Singapore) who are the partners of the ARC Linkage Project.
The primary expectation of the position is to make original and innovative contributions to designing and developing (1) advanced machine learning techniques for traffic incident detection and classification and incident severity prediction and estimation, (2) a data-driven traffic simulation model that allows for multi-level, multi-modal and sub-network simulation, adaptive to dynamic and multi-modal traffic conditions, (3) multi-modal impact analysis techniques for evaluating the impact of an incident on the multi-modal traffic network, and (4) interactive multi-objective optimisation techniques to create appropriate response plans to mitigate incidents. The Fellow will also contribute to implement and evaluate a proof-of-concept system.
The position will be at Level B (equivalent to the Lecturer/Assistant Professor), initially for one-year fixed term and renewable based on the performance.
As part of Swinburne’s Intelligent Data Analytics Lab and the Future Urban Mobility Group in Swinburne’s Smart Cities Research Institute, the Fellow will collaborate with researchers from multiple disciplines, e.g., data science, intelligent transport systems, traffic and transport simulation modelling, machine learning and optimisation, and have access to Swinburne’s Virtual Smart Mobility Research Facility and Swinburne’s supercomputer OzSTAR (featuring 230 NVIDIA Tesla P100 GPUs) to conduct research.
About Swinburne University of Technology
Swinburne is a multi-sector university of science, technology and innovation with more than 54,000 students and 5,000 staff globally. We offer postgraduate, undergraduate, vocational education and online education to provide students with a variety of work-relevant pathways. Our mission is to be a world-class university, creating economic and social impact through science, technology and innovation. We aim to deliver future-ready learners, research with impact and innovative enterprise.
About you
To be successful in the role, you will have:
• PhD in computer science, transport engineering or a related field focused on data analytics, simulation modelling, machine learning and/or AI
• A strong publication record in top-tier journals and/or conferences, particularly in the areas of machine learning, data mining, traffic simulation modelling, transport data analysis, and/or intelligent transport systems
• Previous experience in working in a multi-disciplinary team with people from academia, governmental sectors and/or industries
• Demonstrated capability to work independently and with a team for delivering expected outcomes in an agile way and communicating findings to various stakeholders in an effective manner
A full list of selection criteria is available within the position description.
Benefits
To find out more about the extensive range of benefits offered to Swinburne employees please visit
Careers at Swinburne.
How to apply and further information
To view the position description or to start an application please click on 'apply' or ‘begin’ and submit a resume, cover letter and a response to the Key Selection Criteria, as listed in the position description.
For further information about this position, contact Professor Kai Qin - kqin@swin.edu.au
If you are experiencing technical difficulties with your application, please contact the Recruitment team on staffrecruitment@swin.edu.au
Should you require further support for an interview due to special needs or consideration, please contact Maree Norden, Diversity & Inclusion Manager at inclusion@swin.edu.au. For support or queries related to Aboriginal and Torres Strait Islander employment, please contact DeadlyCareers@swin.edu.au
Agency enquiries will not be accepted for this position.
Swinburne is a large and culturally diverse organisation. We are proud of our commitment to equity and inclusion through key initiatives such as our Charter of Cultural Diversity, Pride@Swinburne Strategic Action Plan, Science in Australia Gender Equity (SAGE) Action Plan and our Reconciliation Action Plan. Equity and diversity are integral to our 2025 vision to be a world class university creating social and economic impact through science, technology and innovation.
Applications close at 5pm on Sunday 8th August 2021.
