University of New England Scholarship in Machine Learning for Medical and Educational Data
The University of New England Scholarship in Machine Learning for Medical and Educational Data bridges a gap between two critical sectors, offering a fully funded Higher Degree Research (HDR) opportunity for an ambitious PhD candidate. While most artificial intelligence research focuses strictly on either clinical systems or academic environments, this joint initiative between the University of New England (UNE) and Cogninet Australia applies advanced, shared machine learning techniques to both domains simultaneously.
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The selected researcher will develop innovative predictive models designed to support real-time medical decision-making on one side, while enhancing teaching methods and student engagement in higher education on the other. With applications evaluated on a rolling basis and no fixed closing deadline, the position remains open until a suitable candidate is appointed. Below is a comprehensive overview of the research scope, funding benefits, candidate eligibility, and the steps required to apply.
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A Research Project With Two Very Different Real-World Applications
The University of New England, established in 1938 in Armidale, New South Wales, holds the distinction of being the first Australian university built outside a capital city, and it’s built a lasting reputation for research strength across science, technology, agriculture, education, and health. This particular scholarship sits within UNE’s School of Science and Technology, in partnership with Cogninet Australia, and it’s led by Associate Professor Subrata Chakraborty, who holds a PhD in Decision Support Systems from Monash University and whose own research spans artificial intelligence, optimisation models, data analytics, machine learning, and image analytics, with applications across health, business, agriculture, and education.
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The project itself is dual-purpose. On the medical side, the successful candidate will help develop research-based AI and machine learning models designed to support medical professionals directly — decision-support tools that assist with diagnosis, prognosis, and ongoing patient monitoring. On the education side, the same underlying data analytics approach is applied to improve learning and teaching practices and student engagement within higher education settings. Because both applications share common modelling foundations, the PhD candidate working across them gains genuinely broad, transferable expertise in applied AI rather than a narrowly siloed research focus.
University of New England Scholarship Summary
| Scholarship Name ⇒ | University of New England Scholarship |
| Host Country ⇒ | Australia |
| Study Level ⇒ | PhD |
| Benefits ⇒ | Annual stipend of AUD $36,862, tuition fees covered, Single Overseas Health Cover for international candidates |
| Funded by ⇒ | University of New England (UNE), jointly funded with Cogninet Australia |
| Eligible Countries ⇒ | All countries |
| Application Deadline ⇒ | Open until filled — no fixed closing date; early application is strongly encouraged |
What the University of New England Scholarship Provides
- An annual stipend of AUD $36,862 (2025 rate), tax-free, paid to the candidate in fortnightly instalments — note that this figure is set annually and may be adjusted in later years, so confirm the current rate directly with UNE if you’re applying well after this rate was published
- Full coverage of tuition fees, for the complete 3.5-year duration of the PhD
- Single Overseas Health Cover, provided specifically for International candidates
- Direct industry engagement with Cogninet Australia, including a built-in internship component, giving the successful candidate genuine applied, real-world exposure alongside academic research
The scholarship is tenable for three years and six months of full-time doctoral study — this is the full, fixed duration of support, structured around the standard timeline for a UNE PhD.
Eligibility Requirements
To qualify for the University of New England Scholarship, applicants generally need to meet the following:
- You may be either a Domestic or International applicant — this scholarship carries no nationality restriction
- You must be a new or continuing student — both categories are eligible
- You must be enrolling, or already enrolled, in a Doctoral (PhD) degree, studied full-time
- On-campus study is preferred, though the scholarship listing indicates online study may also be considered depending on your specific circumstances
- Your research must sit within the School of Science and Technology at UNE
- You should have a genuine background in computer science, or a closely related field
- You need sound Python programming skills, since the project’s modelling work is built around this
- You must be available for industry partner engagement and an internship with Cogninet Australia as part of your studies — this isn’t optional, but a required component of the research programme itself
- You must first secure an offer of admission into the PhD degree at UNE before you’re eligible to actually receive the scholarship — admission and scholarship are two connected but separate steps
Required Documents
Rather than a formal, multi-stage application process, this scholarship uses a direct, streamlined submission:
- A one-page cover letter, outlining your interest in and suitability for the specific project
- An updated Curriculum Vitae (CV)
- Both documents combined into a single PDF file
Because admission into the PhD degree itself is a separate prerequisite step, you’ll also need to prepare whatever documentation UNE’s standard doctoral admissions process requires — this scholarship’s own submission (the cover letter and CV) is specifically about expressing interest in this particular project, not a substitute for the university’s formal admissions application.
How to Apply for the University of New England Scholarship
- Review the project details carefully to confirm your background in computer science and your Python programming skills genuinely fit what the research requires.
- Prepare a one-page cover letter specifically addressing your interest in, and suitability for, this particular medical and educational data analytics project.
- Update your CV to clearly reflect your relevant academic and technical background.
- Combine your cover letter and CV into a single PDF file.
- Email that single PDF directly to Associate Professor Subrata Chakraborty at [email protected], including any enquiries you may have about the project in the same message if needed.
- If your expression of interest is well received, expect further discussion or an interview before formal next steps.
- Separately, apply for and secure admission into the PhD degree at the University of New England, since this is a required condition of being eligible for the scholarship — candidates must be offered admission before the scholarship itself can be confirmed.
Application Deadline
There is no fixed closing date for the University of New England Scholarship — applications remain open until a suitable candidate is found and appointed. Because the position closes as soon as the right candidate is identified, rather than on a scheduled date, applying as early as possible is to your advantage, since a strong application submitted early could close the opportunity before a later application is even reviewed.
Frequently Asked Questions
Is this scholarship still open?
Yes, as of this writing. It remains open until filled, with no scheduled closing date, so it could close at any time once a suitable candidate is appointed.
Can international students apply?
Yes. The scholarship is explicitly open to both Domestic and International applicants, with International candidates additionally receiving Single Overseas Health Cover.
Do I need to already be admitted to the PhD programme before applying for this scholarship?
You need to secure admission before the scholarship can be finalised, but the process starts the other way around — you first submit your cover letter and CV expressing interest in the project, and formal PhD admission follows as part of the overall process.
Is on-campus study mandatory, or can I study online?
On-campus study is preferred, though online study may also be considered depending on your circumstances — this is worth confirming directly with Associate Professor Chakraborty during your initial enquiry.
What technical background do I need?
A computer science background with sound Python programming skills is required, given the project’s focus on building AI and machine learning models for data analytics.
Is the internship with Cogninet Australia optional?
No. Availability for industry partner engagements and an internship with Cogninet Australia is a required part of the scholarship, not an optional extra.
Disclaimer: All programme details in this post were verified from the official University of New England scholarship page and Associate Professor Subrata Chakraborty’s listed contact details as of September 2026. Stipend rates are set annually and may be adjusted in later years. Information is subject to change without notice. Always confirm the latest details directly on UNE’s official scholarships page, or by contacting Associate Professor Subrata Chakraborty at [email protected], before applying. ScholarWaka is not affiliated with the University of New England or Cogninet Australia.
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