Skip to content

Data science

Data-driven progress

We have an unparalleled opportunity to use data modelling to inform many areas of research, from identifying who is at greater risk of disease to understanding the impacts of climate change. We can also use big data science to capitalise on the explosion of online and linked data to help improve social systems.

We're making the most of today's data-rich information age, using strong analytical capabilities and advanced modelling and analysis tools to extract reliable knowledge.

Research we're conducting in the field of computational modelling and simulation science includes:

  • computational biology
  • computational materials science
  • industrial mathematics.

Technology for solutions

With world-class computing and visualisation infrastructure, we're uniquely positioned to turn data into knowledge.

Mathematical modelling and computer simulation reduces the need to carry out expensive and often time-consuming experimentation. Our technology helps industry make decisions about production, optimising operations, and designing new processes.

The future of data expertise

We're training the next generation of quantitative scientists alongside our computer and information scientists, and High Performance Computing (HPC) and data visualisation specialists. These experts are developing the hardware and software needed to ensure the integrity of data sets and to derive insights that impact policy and decision making, and shape the society we live in.

Media releases

23 Apr 2018

Queen's Wharf study

QUT will lead a 20-year study on the Queen's Wharf development in Brisbane with an initial three-pronged focus on gambling impact, connectivity and public sentiment.

6 Apr 2018

Better roads essential for safer cycling

A  QUT-led study of Queensland motorists and cyclists recommends that efforts to improve cyclist safety during overtaking events should focus on improving roadway infrastructure.

Related research projects

Research Engineering Facility

The Research Engineering Facility (REF) is a multidisciplinary team consisting of specialist programmers, designers, engineers, pilots and project managers delivering projects and facilities adopted by the QUT Institute for Future Environments and its partners.

Quantitative Applied Spatial Ecology Group

The questions we are researching revolve around wildlife management, biodiversity, invasive species and food security