Postdoctoral Fellow – Mathematics & Statistics
University of New South Wales, Australia. Posted on 27 May 2026.
At UNSW, we take pride in the broad range and high quality of our teaching programmes. Our teaching gains strength and currency from our research activities, strong industry links, and our international nature.
The School of Mathematics and Statistics has more than ninety continuing academic staff, several dozen research staff, and visiting academics. UNSW is the only university in Australia to be ranked in the top 100 in mathematics and statistics by CWTS Leiden, USNews, and QS.
This position is based in the School of Mathematics and Statistics in the Faculty of Science and supports the Australian Research Council funded Discovery Project: Fixing the holes in Bayesian model comparison. The Postdoctoral Fellow (Level B) will develop new fundamental theory in Bayesian model comparison and create fast and scalable methods for its implementation, building on techniques from computational statistics and machine learning.
About the role
Level B – $127,000 – $150,000 plus 17% superannuation and annual leave loading
Fixed term – 3 years. Full‐time (35 hours per week).
Responsibilities
* Engage in individual and/or collaborative research in a manner consistent with disciplinary practice.
* Create scholarly impact recognised by peers through publishing in high‐quality statistics and machine learning journals and conference proceedings, and releasing supporting open‐source software.
* Conduct research and scholarly activities under limited supervision, either independently or as a member of a team, including attending and presenting research in seminars and conferences and organising research events.
* Establish a personal research portfolio and begin developing independent research proposals.
* Contribute to the development of applications for competitive funding under senior guidance, where appropriate.
* Mentor and guide students and colleagues, developing the next generation of academics through supervision of HDRs and other research students, as merited.
* Teach appropriate courses in Statistics and Data Science, should the opportunity arise and by mutual agreement.
* Align with and actively demonstrate the Code of Conduct and Values.
* Cooperate with all health and safety policies and procedures of the university and take all reasonable care to ensure your actions or omissions do not impact on the health and safety of yourself or others.
Qualifications
* PhD in Bayesian statistics and computation.
* Knowledge of and research experience in several of the following areas: Bayesian statistical theory, Bayesian model comparison, Monte Carlo algorithms for posterior simulation (e.g., MCMC, SMC), variational inference, trans‐dimensional algorithms for Bayesian model comparison, generalized Bayesian inference.
* Proven commitment to proactively keeping up to date with discipline knowledge and developments.
* Demonstrated track record in research with outcomes of high quality and high impact, evidencing the desire and ability to achieve research excellence and the capacity for research leadership.
* A track record of significant involvement with the profession.
* High‐level communication skills and ability to network effectively and interact with a diverse range of students and staff.
* Demonstrated ability to work in a team, collaborate across disciplines and build effective relationships.
* Evidence of highly developed interpersonal and organisational skills.
* Understanding of and commitment to UNSW's aims, objectives and values in action, together with relevant policies and guidelines.
* Knowledge of health and safety responsibilities and commitment to attending relevant training.
UNSW is committed to equity, diversity and inclusion. Applications from women, people of culturally and linguistically diverse backgrounds, those living with disabilities, members of the LGBTIQ+ community, and people of Aboriginal and Torres Strait Islander descent are encouraged. UNSW provides workplace adjustments for people with disability and access to flexible work options for eligible staff. The University reserves the right not to proceed with any appointment.
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