2024 RTP round - Machine Learning and Generative AI Enabled Proactive Mental Health Monitoring

Curtin University, Australia

Updated: 25 days ago
Location: Perth, WESTERN AUSTRALIA
Deadline: The position may have been removed or expired!

Status: Closed

Applications open: 7/07/2023
Applications close: 25/08/2023

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About this scholarship

This research project focuses on leveraging machine learning and generative AI techniques for proactive mental health monitoring. The project comprises three primary research packages that address distinct aspects of the overall goal. The development of advanced machine learning models tailored for proactive mental health monitoring aims to improve detection accuracy and efficiency. The integration of generative AI techniques facilitates personalised mental health support by generating tailored interventions and content. Real-time data fusion and context-aware monitoring provide a holistic understanding of individuals' mental well-being. The project aims to contribute to the development of proactive mental health monitoring systems that detect early signs of mental health issues and provide timely and personalised interventions. 

• Develop advanced machine learning models for proactive mental health monitoring.
• Integrate generative AI techniques to provide personalised mental health support.
• Employ real-time data fusion and context-aware monitoring for a comprehensive understanding of mental well-being.
• Contribute to the development of proactive mental health monitoring systems. 

Research Package 1: Develop Advanced Machine Learning Models

  • Explore deep learning, reinforcement learning, and Bayesian models for proactive mental health monitoring.
  • Design models that accurately detect early signs of mental health issues.
  • Enhance computational efficiency for real-time monitoring.
  • Investigate subtle changes in individuals' mental well-being using diverse data sources.

Research Package 2: Integrate Generative AI for Personalised Support

  • Integrate generative adversarial networks (GANs) and variational autoencoders (VAEs) into proactive mental health monitoring.
  • Personalise interventions, coping strategies, and therapeutic content based on individuals' data and preferences.
  • Address ethical considerations such as privacy and informed consent in generating personalised support.

Research Package 3: Real-time Data Fusion and Context-Aware Monitoring

  • Integrate multiple data sources, including physiological signals, environmental context, social interactions, and self-reported information.
  • Analyse real-time data streams using machine learning and generative AI techniques.
  • Provide continuous monitoring and contextually relevant insights and interventions for individuals' mental well-being. 

This project holds significant implications for proactive mental health monitoring. By developing advanced machine learning models, it aims to enhance the accuracy and efficiency of detection algorithms, enabling early intervention. The integration of generative AI facilitates personalized support, tailoring interventions to individuals' needs and preferences. Real-time data fusion and context-aware monitoring offer a comprehensive understanding of individuals' mental well-being in real-world contexts. Ultimately, this research project strives to contribute to the development of proactive mental health monitoring systems, advancing the field and improving the well-being of individuals. 


  • Future Students

  • Faculty of Science & Engineering
    • Science courses
    • Engineering courses

  • Higher Degree by Research

  • Australian Citizen
  • Australian Permanent Resident
  • New Zealand Citizen
  • Permanent Humanitarian Visa

  • Merit Based

The annual scholarship package (stipend and tuition fees) is approx. $60,000 - $70,000 p.a.

Successful HDR applicants for admission will receive a 100% fee offset for up to 4 years, stipend scholarships at the 2023 RTP rate valued at $32,250 p.a. for up to a maximum of 3 years, with a possible 6-month completion scholarship. Applicants are determined via a competitive selection process and will be notified of the scholarship outcome in November 2023. 

For detailed information, visit: Research Training Program (RTP) Scholarships | Curtin University, Perth, Australia.


Scholarship Details

1


All applicable HDR courses


We are looking for a self-motivated PhD candidate with excellent academic record, expertise in Python and machine learning.


Application process

This project has identified a preferred candidate and is no longer available.  Please review remaining scholarships projects . 


Enrolment Requirements

Eligible to enrol in a Higher Degree by Research Course at Curtin University by March 2024.


Enquiries

The Project lead has identified a preferred candidate and is no longer accepting applications. Please click here to review remaining scholarships projects. 



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