PhD Scholarship in Multimodal Federated Learning and Medical Image Analysis

Job No.: 695949

Location: Clayton campus

Employment Type: Full-time

Duration: 3-year and 3-month fixed-term appointment

Remuneration: $37,145 (tax-free RTP stipend)

For scholarship procedures and conditions, please see: https://www.monash.edu/graduate-research/future-students/scholarships/scholarship-policy-and-procedures.

The Faculty will provide the tuition fee scholarship and Single Overseas Health Cover (OSHC) for the successful international awardee.

The Opportunity

Expressions of interest are sought from outstanding candidates for PhD study in the Department of Data Science and Artificial Intelligence at the Faculty of IT, Monash University.

As part of this scholarship, the successful candidate will develop novel federated learning and multimodal deep learning models for healthcare. The project will focus on enabling privacy-preserving learning from distributed healthcare data sources, including longitudinal medical imaging, electronic health records, pathology, and other clinical data. The candidate will investigate novel approaches for multimodal representation learning, foundation model adaptation, and federated learning to improve disease diagnosis, risk prediction, and clinical decision support. Applications will include chronic diseases such as cancer, diabetes, and rheumatoid arthritis. This research forms part of the National Infrastructure for federated learNing in DigitAl health (NINA), a national initiative aimed at advancing privacy-preserving AI infrastructure and analytics for healthcare across Australia.

The candidate will be supervised by Dr Yasmeen George (yasmeen.george@monash.edu), and will work closely with collaborators across medicine, healthcare organisations, and industry partners.

Candidate Requirements

As the successful candidate, you will:

Have a relevant Honours or Masters degree with H1 or equivalent;

Meet the eligibility criteria for PhD candidature at Monash University. You can check the minimum entry requirements for the PhD. Applicants must also satisfy Monash’s English Language Proficiency requirements;

Demonstrate knowledge of machine learning, medical image analysis, computer vision, federated learning, foundation models, adaptation techniques, multimodal learning, longitudinal image analysis or related areas, evidenced through coursework, research projects or publications;

Have experience with Python programming and the use of high-performance computing infrastructure for AI research;

Have excellent written and verbal communication skills;

Have the ability to work independently, as well as part of a team;

Have the ability to plan, organise, manage multiple tasks and meet deadlines;

Have analytical thinking, data analysis and critical problem-solving skills; and

Be enrolled full time and on campus. Applicants who already hold a PhD will not be considered.

Application Process

To apply for this position please follow the steps below:

Submit an Expression of Interest (EOI) to Dr Yasmeen George (yasmeen.george@monash.edu)

Candidates should include the following when submitting an EoI:

A cover letter not exceeding 500 words that includes a brief statement of suitability and why you are interested in pursuing a PhD in this research project/area.

A curriculum vitae, including a list of any published works, conference presentations and relevant work experience

A full statement of academic record, supported by scanned copies of relevant certified documentation

A copy of the applicant’s honours or master thesis

Contact details of two academic referees

EOIs should be sent via email to yasmeen.george@monash.edu, preferably as a single PDF attachment. Please use subject line: “EOI-[your name]-NINA-PhD”.

Shortlisted candidates will be interviewed (over Zoom if necessary). The interviews will be conducted in English.

Submit a PhD candidature application

The successful candidate will be invited to submit a full application for Monash PhD candidature.

Enquiries: Dr Yasmeen George, yasmeen.george@monash.edu

Applications Close: Monday 31 August 2026, 11:55pm AEST

Supporting a diverse workforce

For instructions on how to apply, click on the "Apply" button above for the full position description and instructions on how to apply for Monash jobs.