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PhD Scholarship - Machine learning approaches to advance cycling and safety

Location: School of Public Health and Preventive Medicine, 553 St Kilda Road, Melbourne Employment Type: Full-time Duration: 3-year fixed-term appointment Remuneration: The successful applicant will receive a tax-free stipend, at the current value of $29,500 per annum 2021 full-time rate, as per the Monash Research Training Program (RTP) Stipend A fully-funded PhD scholarship is available in machine learning approaches applied to cycling. This PhD project is part of a multi-university, multi-disciplinary and international collaboration funded by an Australian Research Council (ARC) Discovery Project (DP210102089). The central goal of this project is to develop a universal platform for modelling cycling volumes. This platform will model the number of cyclists on each road in a city, enabling us to address significant knowledge gaps in cycling safety, identify areas in which we need enhanced cycling infrastructure and evaluate the effectiveness of existing infrastructure. Overall, we anticipate the use of these data will result in improved safety for cyclists, lower injury rates, increased cycling participation and reduced inequities. The specific PhD program will contribute to this project by developing a machine learning model to predict known cycling counts. The PhD program will be hosted by the Sustainable Mobility and Safety Research Group (Dr Ben Beck) in the School of Public Health and Preventive Medicine. This interdisciplinary PhD program will also involve close collaborations and supervision from academics in the Department of Data Science and AI (Dr Shirui Pan), the Department of Civil Engineering (Professor Hai Vu) and the University of New South Wales (Dr Meead Saberi). The vision of the Sustainable Mobility and Safety Research group, headed by Dr Ben Beck, is to make bike riding and walking the leading modes of travel of the future, generating substantial gains in population and environmental health. The group conduct world-leading interdisciplinary research in partnership with government, industry, not-for-profit organisations and the community, advancing the sustainability, equity and safety of mobility. The group brings together experts in injury prevention, road safety, urban and transport planning, public health, and engineering. The group’s research is grounded in robust and diverse research methods across the domains of epidemiology, statistics, spatial analytics, data linkage, machine learning and qualitative methods. They push the boundaries of technology and innovation in pursuit of global challenges, and we foster the next generation of interdisciplinary systems thinkers. Eligibility requirements: Candidates will need to fulfil the Monash University minimum requirements for admission to a PhD detailed here: It is expected that applicants will have expertise in artificial intelligence and data science methods, with some knowledge of deep learning, graph theory and/or transport modelling. The project is based in Melbourne, Australia, and is available for immediate start. Due to current international border restrictions imposed by the Australian Government, the candidate must be living in Australia. How to apply To express your interest in this scholarship and PhD research opportunity, we request candidates provide: A cover letter describing your research interests and why you would like to undertake a PhD (maximum one page); A CV including qualifications, academic achievements, list of publications, work history and references; A copy of your academic transcript(s). Enquiries Please submit your application via email to Dr Ben Beck: 
Essential criteria: 
Minimum entry requirements can be found here:
cycling, safety, machine learning, deep learning, artificial intelligence
Epidemiology and Preventive Medicine
Available options 
Time commitment 
Top-up scholarship funding available 
Year 1: 
Year 2: 
Year 3: 
Physical location 
School of Public Health and Preventive Medicine, 553 St Kilda Road, Melbourne VIC 3004
Hai Vu
Shirui Pan
Meead Saberi

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