Postdoctoral Scientist - Regulatory Systems - Darlinghurst

Fulltime, 12 month contract

Onsite, Darlinghurst, Sydney, NSW​

Reporting to: Faculty, Regulatory Systems – Wong Lab

You are an ambitious computational biologist who is excited by big biological questions and motivated by the opportunity to uncover how our genes are regulated across development, ageing and disease. You enjoy working at the intersection of genomics, statistics and machine learning, and thrive on transforming complex datasets into meaningful biological discoveries.

You’ll bring strong analytical and programming skills, a collaborative mindset and a curiosity-driven approach to research. Whether you’ve already established yourself in the single-cell genomics space or are looking to take the next step in your research career, you are keen to contribute to impactful science within a supportive and highly collaborative environment.

The Regulatory Systems Laboratory, led by Associate Professor Emily Wong, and help answer fundamental questions about how gene regulation is encoded within DNA. Working with cutting-edge single-cell and multiomic datasets, you’ll investigate how gene-regulatory mechanisms change across different cell types, during ageing and in response to genetic variation.

This is an exciting opportunity to work alongside experimental and computational researchers, applying advanced statistical and machine learning approaches to large-scale genomic data. You’ll have the opportunity to drive innovative research projects, contribute to high-impact publications and collaborate with leading national and international research partners.

• Leading the computational analysis of large-scale single-cell and multiomic genomic datasets
• Integrating diverse genomics datasets to uncover changes in cell states and gene regulation
• Applying and developing computational, statistical and machine learning approaches to answer complex biological questions
• Building robust, reproducible analysis pipelines and maintaining high standards of data management and documentation
• Translating computational findings into biological insights in partnership with experimental researchers
• Contributing to scientific publications, conference presentations and grant applications
• Collaborating with researchers across the Institute and external partner organisations
• Mentoring students and junior researchers and contributing to a positive, inclusive laboratory culture​

• Completed, or are close to completing, a PhD in computational biology, bioinformatics, genomics, statistics, computer science or a related quantitative discipline
• Experience analysing single-cell genomic datasets such as scRNA-seq, scATAC-seq or multiome data
• Confident using Python and/or R and working with large datasets in Linux or high-performance computing environments
• Strong knowledge of genomics, genetics and the biological mechanisms that regulate gene expression
• Ability to critically evaluate data, solve complex problems and work independently to drive projects forward
• A publication record appropriate to your career stage, including first-author publications or preprints
• You communicate effectively and enjoy collaborating with researchers from diverse scientific backgrounds
• Experience with machine learning, deep learning, regulatory genomics or ageing biology will be highly regarded​

The Victor Chang Cardiac Research Institute (the Institute) is dedicated to finding cures for cardiovascular disease and is one of the most respected heart research facilities in the world. Renowned for the quality of its breakthroughs, the Institute was established in 1994 and has grown to a team of more than 200 researchers from around the globe, working across five divisions.

A global centre-of-excellence, it integrates Cardiac Physiology and Transplantation, Developmental and Stem Cell Biology, Molecular Cardiology and Biophysics, Structural and Computational Biology, as well as Vascular Biology. At the Institute, we translate our discoveries into new diagnostics, prevention, and treatment of cardiovascular disease - reducing the incidence, severity, and impact of heart disease.