Candidates may develop their own research project in collaboration with their supervisor or apply for one of our available projects.
Applicants who are interested in a specific project should first contact the supervisor listed and then proceed to the Entry Requirements and Apply Now pages.
If you also intend to apply for a scholarship, please see our Scholarships page.
The following projects are currently available:
31st December 2020*
Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.
*unless filled earlier
A study of animal behaviour is potentially the most powerful indicator of animal welfare (Dawkins, 2004). The incorporation of technologies (e.g. camera) into dairy farms does allow the behaviours of dairy cattle to be closely monitored but does not provide a level of intelligence to support decision making, such as associating changes in behaviour patterns with wellbeing. Recent developments in artificial intelligence (AI) have driven great progress in modelling human activity and behaviour using data obtained from visual sensors (Herath et al., 2017).
In this research, the same idea could be used to monitor the behaviour of dairy cattle or calves in real time. Advanced techniques such as deep learning (Lecun et al., 2015), which outperformed humans in several tasks, can be applied to identify behaviour patterns and link them to animal welfare. For example, changes in behaviour (i.e. from high to low activity, decline or absence of positive behaviour such as play, visits to the water trough/feeder) are a sensitive, sub-clinical indicator of poor health. This research aims to develop an innovative and global leading technological system to monitor wellbeing of dairy cattle, using state-of-art AI and machine learning technologies. Dawkins, M. S. (2004). Using behaviour to assess animal welfare. Animal Welfare, 13(Suppl), S3-S7. Samitha Herath, Mehrtash Harandi, Fatih Porikli. Going deeper into action recognition. Image Vision Computing. Volume 60 Issue C, Pages 4-21, 2017. Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Deep learning. Nature. Volume 521, pages 436–444 (28 May 2015).
This project will be based at the Newnham Campus, Launceston, TAS.
See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree
Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.
Information about scholarships is available on the Scholarships webpage.
Please contact, Dr Winyu Chinthammit for further information.
31st December 2020*
Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.
*unless filled earlier
Ageing is associated with increased feelings and experiences of loneliness and social isolation. Research shows that loneliness can be a particular issue for older Australians who live in rural and remote areas such as Tasmania. Health promotion interventions aimed at reducing social isolation in the elderly suggest that interventions where individuals are required to actively participate are more effective than passive interventions.
This innovative project seeks to assess the extent to which a novel technological intervention “CompanionBot” can help reduce the prevalence of social isolation and loneliness for elders. The project seeks to use a novel combination of Artificial Intelligence and Internet of Things to track an older person’s movement and patterns of daily activities in their home to provide early detection of social isolation and reduce loneliness through conversational engagement with an AI-powered conversational bot. This study will offer an opportunity to evaluate the effectiveness of technological interventions with older groups in rural areas to address social isolation and loneliness using both an empirical and qualitative framework.
This project will be based at the Newnham Campus, Launceston, TAS.
See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree
Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.
Information about scholarships is available on the Scholarships webpage.
Please contact, Winyu Chinthammit for further information.