Agency, critical thinking, judgement, taste, and reliable AI systems that can operate beyond one-shot prompting.
Scholarships
PhD opportunities across AI Future, AI for Science, and AI for Health.
The Applied Artificial Intelligence Institute at Deakin University regularly supports PhD candidates interested in frontier AI, scientific discovery, and clinically meaningful health systems.
What we look for
Strong technical ability, research curiosity, evidence of sustained academic performance, and a clear fit with one or more of the core research platforms and live build environments.
Scientific copilots, materials, molecules, inverse design, autonomous research workflows, and lab-facing discovery systems.
Clinical AI assistants, imaging, longitudinal health modelling, mental health, monitoring, public health, and deployable care systems.
- Potential for a strong research career, not only successful degree completion
- Excellent academic performance in CS, mathematics, physics, or related areas
- Research experience, publications, awards, or strong honours-level work
- Clear intellectual fit with the group’s research directions
- CV
- Academic transcript
- Short note explaining fit with the research
- Any relevant papers, thesis work, or project portfolio
Research Environment
Students here can join live systems work, not only paper ideas.
Current directions include expert-agent systems, scientific discovery engines, AI-assisted mathematics, embodied intelligence, health AI sandboxes, and AI-native research training infrastructure.
For future AI researchers
Join work on Ark, agent architectures, invention systems, judgement, and the physics of intelligence.
For science-oriented students
Join discovery workflows such as Einstein and Gauss, where research means hypotheses, benchmarks, theorem handoff, and formal reasoning.
For translational builders
Join health and deployment-facing builds such as evidence-centric MRI generation, clinical workflows, and grant-ready system programs.
Doctoral Expectations
What a PhD in AI now means here.
The AI world has changed quickly. Strong applicants should assume that coding, implementation, and many baseline research moves are already AI-accelerated.
Read the full manifestoIndependent investigator
A PhD is about conceiving ideas, forming a plan of attack, executing with AI and other tools, and then turning the result into a paper, patent, or inventive product.
Novelty above AI baseline
Novelty is no longer measured only against the literature. You should also ask what the strongest available AI can already ideate and implement, then go one step beyond it.
Long shelf-life
The target is work that still matters after the current wave moves on. We care much more about durable questions and deep gaps than about incremental benchmark gains.