Research Supervisor Connect
This project aims to leverage advanced neuroimaging and AI-driven analysis to identify individual “brain fingerprints” of Alzheimer’s disease, enabling earlier detection and more precise, personalized therapeutic strategies.
Alzheimer’s disease (AD) is highly heterogeneous, with patients showing diverse trajectories of cognitive decline, pathology, and treatment response. Current clinical tools fail to account for this neurobiological diversity, leading to inconsistent diagnoses and limited therapeutic success.
This project will apply state-of-the-art neuroimaging methodologies—including multimodal MRI (structural, diffusion, and functional imaging), lesion mapping, and connectomics—combined with machine learning approaches such as normative modelling and digital twin frameworks. By integrating large-scale imaging datasets (e.g., UK Biobank, ADNI, AIBL) with clinical and cognitive data, we aim to map distinct subtypes of AD, uncover early biomarkers, and predict disease progression on an individual level.
The ultimate goal is to translate these findings into clinically actionable tools that can guide precision therapies and improve patient outcomes.
Research techniques/technologies
Multimodal MRI (T1, fMRI, DWI), connectomics, lesion mapping, machine learning (normative modelling, deep learning), 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:
How to apply:
To apply, please email [email protected] the following:
The opportunity ID for this research opportunity is 3688