We are seeking a motivated student to contribute to the development of a digital risk-stratification tool that predicts short-term seizure recurrence in emergency care. This project involves working with free-text ambulance and emergency department clinical notes to extract prognostic indicators and support the fine-tuning of Large Language Models (LLMs) for medical risk prediction. The student will gain hands-on experience in data preprocessing, model development, and evaluation using real-world clinical datasets from one of Australia’s leading epilepsy centres.

This is a deeply interdisciplinary project at the intersection of artificial intelligence, clinical neuroscience, and emergency medicine. Working alongside researchers from the Faculty of IT and clinical experts in neurology and emergency care, the student will help co-design AI tools that address a nationally significant healthcare challenge—reducing avoidable emergency presentations for people with epilepsy. The project offers an opportunity to contribute to translational research with direct clinical relevance and strong potential for broader impact.