MAITRI Grant (DFAT) PhD Scholarship

Status

Open Now

Opens

Closes

Scholarship Value

Stipend total $AU118,500 ($AU38,000 in Year 1, $AU39,500 in Year 2, and $AU41,000 in Year 3) plus Project Support Funding of $AU6,500 for travel support

Length of Scholarship

3 Years / 3 EFTSL

Number Available

2

Funding Type

Research

The purpose of this scholarship is to develop an integrated, intelligent framework for solar farm monitoring and predictive maintenance by combining autonomous drone-based data acquisition, Edge-AI processing, and Digital Twin-driven analytics. The research will span hardware–software co-design, including the development of onboard Edge-AI systems (GPUs/FPGAs) for real-time fault detection, as well as scalable Digital Twin architectures for predictive simulation and performance forecasting using real-time and historical data. 

The project will validate these technologies through pilot deployments in Australia and India, supported by the creation of open datasets and collaborative international research. Ultimately, the scholarship aims to enhance solar energy reliability, reduce maintenance costs, improve energy output, and contribute to the digital transformation and workforce development of the renewable energy sector.

Two PhD scholarships are available, with one scholarship allocated to each of the following projects:

  1. Digital Twin-Driven Predictive Analytics for Fault Forecasting and Maintenance Scheduling of Solar Farms 
  2. Edge-AI Enabled Autonomous Drone Systems for Real-Time Fault Detection in Solar Energy Infrastructure

Eligibility Requirements:

  • A Master’s degree or Honours degree (4 years) in Electrical Engineering, Computer Science, Mechatronics, Robotics, AI, Data Science, or related disciplines.
  • Strong knowledge of renewable energy systems, embedded systems, AI/ML, or cyber-physical systems (e.g. Edge-AI, Digital Twins).
  • Demonstrated interest or experience in either:
    • hardware–software integration (e.g., drones, sensors, GPUs/FPGAs) or
    • data-driven modelling and predictive analytics.
  • Enthusiastic, self-motivated, and capable of conducting independent research and development.
  • Ability to work in multidisciplinary and international collaborative environments, with strong communication skills.
  • Willingness to be based in Rockhampton QLD (Project 1) or Melbourne VIC (Project 2) and undertake research visits to partner institutions in India.
  • A valid Working with Children Check (or ability to obtain prior to or upon commencement).
  • English language proficiency with a minimum score of IELTS 6.5 (with no band less than 6.0) or an equivalent PTE Academic score of 62.

Desired Skills:

  • Experience with drone systems, IoT sensors, or real-time data acquisition.
  • Knowledge of machine learning, deep learning, or large-scale data analytics.
  • Programming experience (e.g., Python, MATLAB, or similar).
  • Familiarity with Digital Twin technologies, simulation platforms, or power/energy systems.
  • Prior research or industry experience relevant to AI, robotics, or renewable energy.
  • Interest in publishing in peer-reviewed journals and contributing to open datasets. 

Submit the supporting documentation to Prof. Jamshid Aghaei j.aghaei@cqu.edu.au and Dr Ahsan Morshed a.morshed@cqu.edu.au as soon as possible. Applicants may be invited to meet with the research team to discuss their application. Please reach out to Prof Aghaei and Dr Morshed if you would like more information. 

Applicants will be notified of the outcome of their application via email as soon as an outcome is available.

  1. CV
  2. Cover letter addressing the selection criteria
  3. Academic transcripts
  4. 3-year research plan, including motivation, state-of-the-art, research questions or hypotheses and publication plan

The preferred applicants will need to be admitted to CQUniversity as a PhD student, by lodging a successful application for PhD admission through CQUniversity’s online application portal.