Research Fellow, statistical omics

Company Description The Le Cao Lab, established in 2008, is a globally recognised research group within Melbourne Integrative Genomics and the School of Mathematics and Statistics at the University of Melbourne. Led by Professor Kim-Anh Lê Cao, a recipient of multiple NHMRC fellowships and the Australian Academy of Science Moran Medal, the lab focuses on computational statistics and biological data integration. The team develops efficient methods and software to interpret large-scale omics datasets, including the widely used mixOmics R toolkit for multivariate data analysis. Their algorithms support real-world applications such as protecting the Great Barrier Reef, enhancing agricultural productivity, and identifying biomarkers for major diseases. The lab is highly collaborative, well-funded, and committed to mentoring emerging researchers who go on to successful international careers.
Role Description</strong> The Research Fellow is a full-time, on-site role based in Melbourne. The position involves developing and applying statistical and computational methods for the integration and analysis of large-scale omics datasets, including transcriptomics, metabolomics, and other high-dimensional biological data. Day-to-day activities include designing analyses, implementing algorithms, writing reproducible code, and collaborating with experimental scientists and industry partners to translate data into biological insight. The Research Fellow will prepare manuscripts, present findings at seminars and conferences, contribute to grant applications, and participate in mentoring and training activities for students and early-career researchers. The role also includes maintaining and extending existing software tools, such as mixOmics, and engaging with the user community to support best practice in data analysis.
Qualifications Advanced skills in statistical modelling and multivariate analysis for high-dimensional omics or biomedical data.Proficiency in programming languages commonly used in data science, such as R and/or Python, including package or software development experience.Experience with bioinformatics workflows, data integration across multiple omics platforms, and handling large, complex datasets.Strong background in quantitative disciplines such as statistics, applied mathematics, computational biology, or related fields.Demonstrated record of research outputs, including peer-reviewed publications and presentations in relevant scientific areas.Effective communication skills, including the ability to explain complex methods to interdisciplinary collaborators and document analyses clearly.Ability to work collaboratively in a diverse research environment, manage multiple projects, and meet research milestones independently.PhD (or near completion) in statistics, bioinformatics, computational biology, or a closely related discipline; experience with omics data is highly desirable.Familiarity with reproducible research practices (version control, literate programming, containerisation) and modern data science workflows is beneficial.
Note: Australian working rights are required for this position.