Level A $112,550 – $120,621 plus 17% superannuation and annual leave loading
Full-time, 2-year fixed-term position, with the possibility of a one-year extension
Macquarie University, Wallumattagal Campus, North Ryde
Macquarie University is hiring for a Research Associate, Statistical Modelling for High-Dimensional Data. This position is open to statisticians or data scientists with a strong research track record in statistical modelling, data science, or related fields, and a commitment to interdisciplinary and applied research.
This position is funded by the Australian Research Council Discovery Project, and the successful candidate will join Macquarie University's Statistical Modelling Research Group, led by Professor Samuel Muller and Dr Houying Zhu and collaborate with Associate Professor Garth Tarr (University of Sydney) and Associate Professor Tania Garcia (University of North Carolina at Chapel Hill).
The appointee will contribute to research and the supervision of PhD and Master of Research (MRes) students in one or more of the following areas:
Variable selection and inference for statistically challenging data, including longitudinal, clustered, and correlated univariate and multivariate responses.
Robust, scalable, and computationally efficient stability selection methods that integrate frequentist and Bayesian strengths, with visualisation tools to assess feature stability.
Statistical methods for analysing multi-layered and structured data in bioinformatics, health and agriculture applications.
About us
An inclusive community in scientific collaboration. Here at the School of Mathematical and Physical Sciences, Mathematics and statistics drive breakthroughs in technology and data science, while physics and astronomy seek to understand matter and the Universe, everything from quarks to galaxy clusters.
We collaborate closely with local, national, and international organisations and companies across the diverse mathematical and physical sciences fields. Please see the link below for more information.
School of Mathematical and Physical Sciences | Macquarie Uni
About You (Selection Criteria)
A PhD in Statistical Modelling or a closely related discipline in Statistics, Data Science, Mathematics, or a related quantitative field, either awarded or submitted for examination prior to commencement of the position.
Demonstrated ability to publish high-quality research in peer-reviewed scientific journals.
Ability to assist in the supervision of postgraduate research students.
Demonstrated capacity to work both independently and collaboratively within a research team.
Experience presenting research findings at seminars, conferences, and/or other scientific meetings.
Strong written and verbal communication skills.
Advanced computational and programming skills, particularly in R.
Demonstrated ability to attract external research funding through grant applications and/or contribute to funded collaborative research projects with internal and external partners. (Desirable)
Research experience in statistical modelling for high-dimensional and complex data, including areas such as variable selection, Bayesian methods, or related methodologies. (Desirable)
Experience in developing statistical software, reproducible research workflows, or opensource research tools. (Desirable)
How to apply
To be considered for this role, candidates are requested to submit a resume and a separate cover letter addressing the selection criteria. You may also submit a Research Statement outlining your research achievements to date and their future research directions.
Applications Close
Applications for this role will close on Monday 27th July at 11 PM.
Role specific enquiries: Dr Houying Zhu, Senior Lecturer in Statistics via email [email protected].
Why Join Us?
Macquarie is a university engaged with the real and often complex problems and opportunities that define our lives. Since our foundation over 60 years ago, we have aspired to be a different type of university. Over the years, we have grown to become the centre of a vibrant local and global community. Connect with us today.
We rank amongst the top employers in the Australian Workplace Employers Index. Explore the fantastic benefits available to you when you become part of the team at Macquarie University.
Flexible, hybrid work arrangements
17% Superannuation
Subsidised onsite parking options
Extensive training and professional development programs
Onsite childcare facilities to support working parents
Onsite vacation care during school holidays
Discounted health insurance
Subsidised membership at our Sport & Aquatic Centre
Access to an Employee Assistance Program for free and confidential support
Convenient onsite GP, imaging, and physiotherapy clinics
Pre-Employment Checks
Macquarie University cultivates a workplace defined by safety, ethical conduct, and strong integrity. Prior to completion of an offer of employment, preferred candidates will be required to participate in a combination of pre-employment checks relevant to the role they have applied for.
Your employment is conditional upon the completion and maintenance of all role-required pre-employment or background checks in terms satisfactory to the University.
If you're already part of the Macquarie Group (MQ University, U@MQ, MQ Health, MGSM), you'll need to apply through your employee Workday account.
Applications Close:
27/07/2026 11:59 PM
A Place Where You Belong
At Macquarie, we believe diversity makes us stronger, inclusion drives our success, and belonging inspires us to do our best work. We are proud to foster a community where different backgrounds, identities, and experiences are valued, and where our people are empowered to thrive through supportive leadership, shared responsibility, and a deep commitment to genuine care and respect for our community. Find out more about our vision for a truly inclusive workplace in our Diversity, Inclusion and Belonging Framework.
Flexible Work
At Macquarie, we believe by providing flexibility in when, where and how work is done, we can support our staff to manage their personal commitments, while optimising their work performance and contributions to the University.
