The School of Mathematics and Statistics (SMS) at the University of Sydney (USyd) is seeking up to two outstanding PhD students to join our team in developing new theory, methods, and applications of statistical dimension reduction for data with complex structures, including but not limited to, multi-way clustered, spatial-temporal, and network dependence. The students will be undertaking research in connection with the Australian Research Council Discovery Project DP260100579, addressing important challenges of distilling high-dimensional regression and classification relationships, with little to no loss of information, into results readily understood by domain experts.
The PhD students will be based in SMS, a centre for cutting-edge mathematical research in Australia. To date, USyd is the only Australian university to have received the highest rating (5 out of 5) for research in the mathematical sciences in every Australian Research Council Excellence in Research for Australia. The statistics group within SMS has a highly collegial atmosphere. Furthermore, the students will also join the Sydney Precision Data Science Centre, engaging in high-quality multidisciplinary research and connecting with interdisciplinary researchers in data-intensive science.
The supervision team will include Dr Linh Nghiem, Dr Shila Ghazanfar, A/Prof Rachel Wang, all at SMS, along with A/Prof Francis Hui at The Australian National University.
Requirements
All candidates are required to apply for the RTP scholarship. Interested candidates are encouraged to send their CV, (unofficial) academic transcripts, and (optional) a research proposal/statement of purpose to Dr. Linh Nghiem at linh dot nghiem “at” sydney dot edu dot au. Potential candidates will be invited to have an interview before applying.
Submission deadline for admission and scholarship is on 19 December 2025 (for International students) or 30 March 2026 (Domestic students), for more information, visit this link here.
If you have any questions, please reach out to Dr. Linh Nghiem.