Degree type
PhD
Closing date
1 July 2023
Campus
Hobart
Citizenship requirement
Domestic
About the research project
The development of conversational AI systems has witnessed significant advancements in recent years. With the advent of natural language processing (NLP) and deep learning techniques, chatbots have become more advanced and interactive. However, there are still challenges in developing systems that can understand human emotions and respond accordingly. The proposed project aims to address this issue by incorporating facial emotion recognition into conversational AI systems.
The primary objective of the project is to develop a conversational AI system that can accurately recognize facial emotions and respond appropriately to the user's emotional state. The project will focus on developing a multi-modal deep learning model that integrates facial emotion recognition with intent recognition, sentiment analysis, and response generation.
The project will leverage edge AI to enhance the accuracy and efficiency of the conversational AI system. The model will be trained on a large dataset of facial expressions and associated emotions, as well as on natural language data. The training data will be sourced from publicly available datasets and will be augmented with synthetic data to improve the robustness of the model.
The expected outcomes of this project include a conversational AI system that can accurately recognize facial emotions and respond appropriately to the user's emotional state. The system will also be designed to adapt to the user's emotional state over time, thereby improving the quality of interactions between the system and the user. Furthermore, the output of the project will be valuable for various application developments, including the health domain. For example, the project can help in effective pain management in hospital care, where traditional methods for assessing pain can be subjective and may not accurately reflect the patient's experience.
Primary Supervisor
Meet Dr Mira ParkFunding
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.
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:
- Research skills
Additional desirable selection criteria specific to this project:
- Python
Application process
- Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
- Contact Dr Mira Park 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 1 July 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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