Postdoctoral Research Associate In Statistics – University …, City of Sydney

Full time, 2 years fixed term, located on the Camperdown Campus at the School of Mathematics and Statistics

Chance to contribute to cutting‑edge statistical research and be part of a multi‑institutional team with strong expertise across statistical methodology, computation, and high‑impact scientific applications.

About the opportunity

The School of Mathematics and Statistics (SMS) is currently seeking a Postdoctoral Research Associate in Statistics to develop recent 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 successful applicant will be jointly supervised by CIs Linh Nghiem, Rachel Wang, and Shila Ghazanfar at SMS, and Francis Hui at the Australian National University (ANU).

Responsibilities

Your key responsibilities will be to:

conduct research under ARC DP grant DP with the aim of developing contemporary sufficient dimension reduction techniques for complex dependent data

contribute to writing statistics and interdisciplinary journal articles, along with other research outputs such as research software

present results of research at conferences and other venues

assist with the supervision and mentoring of postgraduate students as appropriate.

About you

The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance of recruiting talent aligned to these values and are looking for a Postdoctoral Research Associate who has:

a PhD (or close to completion) in Statistics or a closely related area

a demonstrated track record of research outputs in statistical theory and methodology, as evidenced by publications in relevant peer‑reviewed journal articles, presentations at seminars and conferences, and student awards/prizes in statistics (for instance)

familiarity with statistical software, preferably with R and/or Python

demonstrated ability to work independently and as part of a team with minimal supervision

excellent communication (oral and written) skills in the English language, and a demonstrated ability to interact effectively and appropriately with a variety of staff and students in an academic environment.

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