Degree type
PhD
Closing date
25 September 2023
Campus
Launceston
Citizenship requirement
Domestic / International
About the research project
The Internet of Things (IoT) accumulates large quantities of data, often in the form of multiple time series from different sensors. Certain events occur in regular intervals within time series, while others arise accidentally. Sometimes events start to happen and fall in a pattern, and other times, the events a simply one-offs. In this research, we will investigate the technique to identify such events, their duration, relationships, and the probability of any pattern developing in the future.
The proposed research seeks to evaluate the performance of the deep learning-based model in predicting events in raw time series. Moreover, we will investigate whether incorporating information about object interactions improves the model's predictive capabilities. This would involve considering how sensors dynamically relate with each other, which could lead to more accurate and context-aware predictions.
The resultant algorithms and techniques can be used in numerous domains, including smart buildings, vehicles and agriculture. This project will focus on smart buildings to create a dynamic real-time mapping of every part of buildings covered by sensors and unobserved regions. The outcome of this project will help trace events as the cause of other events and establish a pattern of cascading events. This could help stop some events if they are predicted to lead to further unwanted events and trace their origins.
Primary Supervisor
Meet Dr Ananda MaitiFunding
Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:
- a living allowance stipend of $31,500 per annum (2023 rate, indexed annually) for 3.5 years
- a relocation allowance of up to $2,000
- a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)
If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.
As part of the application process you may indicate if you do not wish to be considered for scholarship funding.
Other funding opportunities and fees
For further information regarding other scholarships on offer, and the various fees of undertaking a research degree, please visit our Scholarships and fees on research degrees page.
Eligibility
Applicants should review the Higher Degree by Research minimum entry requirements.
Ensure your eligibility for the scholarship round by referring to our Key Dates.
Additional eligibility criteria specific to this project/scholarship:
- Applicants must be able to undertake the project on-campus
Selection Criteria
The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.
Additional essential selection criteria specific to this project:
- Solid programming skills
- Good writing skills
- Basic knowledge of artificial intelligence/machine learning
Additional desirable selection criteria specific to this project:
- Knowledge about deep learning
- Experience with Tensorflow or PyTorch
Application process
- Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
- Contact Dr Ananda Maiti to discuss your suitability and the project's requirements; and
- In your application:
- Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
- Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
- Apply prior to 25 September 2023.
Full details of the application process can be found under the 'How to apply' section of the Research Degrees website.
Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.
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