Vector databases are the infrastructure of the AI era. From Retrieval-Augmented Generation in LLMs, to multimodal recommendation, to anomaly detection in finance — every modern AI system relies on fast, accurate similarity search over high-dimensional embeddings. This ANU-based PhD position invites you to build the next generation of vector database systems.
Research Direction
Embedding unstructured data (text, images, audio) into high-dimensional vectors has become the default representation for AI workloads. The system that stores, indexes, and serves these vectors is the vector database — and there are many open problems. The student will pick one or two of the following directions based on their interests:
• Streaming and dynamic indexes. Real-world vector datasets are continually inserted, deleted, and updated. How do we maintain high-recall ANN search without expensive offline rebuilds?
• Hybrid queries. Most production queries combine vector similarity with structured filters (location, time, attributes). How do we co-design index structures that handle both efficiently?
• Disk-aware, billion-scale indexes. When the dataset exceeds memory capacity, the index must live on disk while still serving millisecond queries. What is the right cost model and data layout?
• GPU-accelerated ANN. How do we exploit modern hardware (GPUs, NPUs, specialized accelerators) for both index construction and query processing?
Supervisor
• Primary supervisor: Dr.Mengxuan Zhang (ANU School of Computing) — vector database, ANN search, high-performance query processing
Co-supervisors may be added depending on the chosen sub-direction.
Program Structure
• 3–4 years, full-time at ANU (Canberra, Australia)
• Enrolled at ANU School of Computing
• Research-focused, with weekly one-on-one supervision
• Strong publication culture — VLDB / SIGMOD / ICDE target venues
Funding and Benefits
• ANU PhD stipend: AUD39,069 per annum (Full-time base stipend rate 2026)
• Tuition fee waiver available
• Travel support for top-tier conferences (subject to research outcomes)
Eligibility
• Bachelor’s or Master’s degree, ideally with at least Second-Class (Upper) Honours or equivalent
• Strong programmingbackground — C++ strongly preferred
• Solid foundation in data structures and algorithms
• Familiarity with machine learning / deep learning fundamentals
• Genuine interest in systems research and a willingness to engage with low-level engineering when needed
• Open to both domestic Australian students and international applicants
What You Get from Working in My Lab
• Weekly one-on-one mentoring with hands-on research guidance
• Top-tier publications(VLDB, SIGMOD, ICDE)
• International research network around me
• A pathway to either academic or industry careers in database systems / AI infrastructure
How to Apply
Please email Dr. MengxuanZhang at Mengxuan.Zhang@anu.edu.au with the following:
• Subject line: “PhD Application — Vector Database (ANU-based)”
• Your CV
• Academic transcripts(undergraduate and any postgraduate)
• A short statement (one page or less) describing your research interests and the open vector database problems you find most exciting
• Names and contact details of two academic referees
Strong candidates will be invited for a video interview. Following that, formal applications must be submitted through the ANU PhD admission system. Applications are reviewed on a rolling basis — we encourage early submission.