Artificial Intelligence for Neuroscience and Brain Health

Summary

This project leverages cutting-edge AI - including large language models (LLMs) and foundation models - to accelerate discoveries in neuroscience and develop precision approaches for brain health.

Supervisor

Dr Jinglei Lv.

Research location

Biomedical Engineering

Synopsis

Artificial Intelligence (AI) is transforming neuroscience by enabling new ways to model brain structure, function, and cognition. The next generation of AI - foundation models and LLMs - offer unprecedented opportunities to integrate multimodal data (e.g., neuroimaging, genomics, clinical records, behavioural data) and generate personalised, interpretable insights into brain health.

This project will explore how LLMs and foundation models can be tailored for neuroscience, including:

Neuroimaging analysis: Using foundation models to learn shared representations across MRI, fMRI, DTI, and PET for improved diagnosis and prognosis of brain disorders.
Knowledge integration: Applying LLMs to synthesise biomedical literature, clinical notes, and multi-omics data to uncover new disease mechanisms.
Digital brain twins: Developing personalised foundation models that capture individual variability, predict disease trajectories, and guide therapeutic strategies.
Interpretability and trust: Designing explainable AI tools that link model predictions to neurobiological mechanisms, ensuring clinical reliability.

By uniting AI innovation with neuroscience, the project aims to build a new generation of computational tools that advance understanding of the healthy and disordered brain while driving precision medicine in brain health.

Research techniques/technologies

Large language models (LLMs), foundation models, deep learning, graph neural networks, neuroimaging (MRI, fMRI, DTI, PET), multimodal integration, digital twin modelling.

Offering

A scholarship for 3.5 years at the RTP stipend rate (currently $41,753 in 2025). International applicants will have their tuition fees covered.

Successful candidates must:

have a background in computer science, biomedical engineering, neuroscience, or related disciplines.
have strong coding and analytical skills (Python, PyTorch/TensorFlow).
have an interest in AI for healthcare, neuroscience, and translational applications.

How to apply:

To apply, please email [email protected] the following:

A detailed CV, including academic qualifications, research experience and publications
Academic transcripts

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Opportunity ID

The opportunity ID for this research opportunity is 3686