Project summaries
Project 1: AI-Powered Semantic Reasoning and Typed Computational Systems
This project investigates whether typed computational cells can preserve semantics, authority, provenance, and execution constraints across system boundaries. The goal is to reduce reliance on fragile serialisation mechanisms, duplicated validation processes, and application specific trust logic. The research will explore the design of a generalisable typed systems substrate for durable semantic computing and trustworthy digital infrastructure.
This project explores AI‑powered semantic reasoning and the development of typed computational systems capable of preserving semantics, provenance, authority, and execution constraints across complex digital boundaries. This work aims to reduce reliance on fragile serialisation, duplicated validation, and application‑specific trust logic, contributing to durable and verifiable semantic computing foundations.
Project 2: AI Enabled Biochemistry and Molecular Intelligence
This project applies state of the art deep learning approaches — including protein language models, multi omics integration, and generative biological modelling — to derive new biological insights and address major challenges in molecular biology and biochemistry. The focus is on building AI systems that can reason over biological structure, function, and interaction networks. The expected research output includes robust AI decision support systems and quantum computational foundation enabling more trustworthy, interoperable, and resilient digital ecosystems.
This project focuses on AI‑enabled biochemistry and molecular intelligence, applying state‑of‑the‑art deep learning methods—including protein language models and multi‑omics integration—to generate new biological insights and address major challenges in molecular and cellular systems.
Together, these projects support applications such a triple‑entry accounting, recoverable digital identity, AI powered institutional automation, and biological discovery. Successful candidates will contribute to foundational advances in AI that enable more secure, interpretable, and future‑ready technologies.
Applications close
20 October 2026 11:55pm
To complete an application you will need to create an account and login to the HDR scholarships portal.
Eligibility
1. Domestic applicants only - Australian, New Zealand citizen or Australian permanent resident.
Ideal Candidate
The ideal candidate is a highly motivated, self‑driven individual with a strong work ethic and a genuine passion for advancing knowledge in emerging areas of artificial intelligence, quantum systems, or computational biology. They demonstrate intellectual curiosity, persistence in solving complex problems, and the ability to work both independently and as part of a collaborative research team.
Candidates should be comfortable exploring new ideas, engaging with theoretical and applied research, and contributing to high‑quality scientific publications. A commitment to continuous learning, professional integrity, and responsible research practices is essential. Strong analytical skills, effective communication, and the ability to manage time and research milestones will be highly valued.
This opportunity is well suited to individuals who thrive in challenging research environments, are eager to push the boundaries of current knowledge, and are motivated to make meaningful contributions to trustworthy, resilient, and future‑ready intelligent systems.
Inclusions
The full-time candidature period is 3 years, with the possibility of a 6-month extension. Coverage for the domestic HDR student includes:
- Stipend - $38,000 (AUD) per annum, paid fortnightly and is tax-free for full-time students.
- Tuition fees - Full coverage
- Relocation allowance - Up to $2,000 to relocate to a UniSC campus
All inclusions are in accordance with UniSC RTP Research scholarship conditions
Contact
Professor Elizabeth Chang
Email: echang@usc.edu.au