Multi-modal learning with applications to medical data

Data such as magnetic resonance imaging (MRI) are essential in healthcare for disease understanding and assessment. However, imaging alone cannot allow us to fully understand chronic diseases and neither can one modality of data. Although some progress has been made to improve multi-modal learning within deep learning, there are has been little progress in integrating several modalities and the wealth of data across timepoints. A fully multi-modal system will make disease studies more comprehensive and therefore allow the efficacy of new targeted technologies to be evaluated more accurately, enabling the development of novel treatments in the future.

You will create new artificial intelligence (AI) with multi-modal and explainable capabilities that can be used in large medical studies of the future. This will enable much more detailed analysis of chronic diseases and allow greater understanding of their multi-faceted nature in the hopes of developing earlier interventions in the future.

The successful candidate will join ARC Future Fellow Dr Shekhar S. Chandra’s team of AI researchers at The University of Queensland. Dr Chandra is a leading researcher in imaging and AI, covering diverse areas such as vision, medical imaging, multi-modal and metric learning, chaos theory and fractals, signal processing and number theory.

Dr Chandra also has strong research and industry ties with partners such as CSIRO and Siemens Healthineers (Erlangen, Germany and Brisbane, Australia) and has successfully commercialised medical image analysis software called ChondralQuant as a medical device approved for use in hospital in over 90 countries. UQ’s Research Computing Centre will provide state-of-the-art high-performance computing that will be essential in building the necessary AI models for this project.

This project is supported by the Research project scholarship.

Learn more about the Research project scholarship.

Your application will be assessed on a competitive basis.

We take into account your:

• previous academic record
• publication record
• honours and awards
• employment history.

A working knowledge of imaging (image processing or biomedical), multi-modal learning or computer vision would be of benefit to someone working on this project.

You will demonstrate academic achievement in the field(s) of computer science, engineering, mathematics or physics or bio-informatics and the potential for scholastic success.

A background or knowledge of deep learning is highly desirable.

You must submit an expression of interest (EOI) by 23 March, 2026 23 March, 2026.

Before you apply

1. Check your eligibility for the Doctor of Philosophy (PhD).
2. Prepare your documentation.
3. If you have any questions about whether the project is suitable for your research interests, contact Dr Shekhar Chandra (shekhar.chandra@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the ‘Scholarship/Sponsorship’ section with the following details:

1. Are you applying for an advertised project: 'Yes'
2. Project: 'Research project scholarship'
3. Scholarship Code Listed in the Advertisement: MULTIMODAL-CHANDRA
4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/multi-modal-learning-applications-medical-data

Submit an EOI