Every summer, the Pawsey Supercomputing Research Centre opens its doors to a select group of undergraduate and postgraduate students, giving them the rare chance to work at the cutting edge of science and technology. These interns are not just learning in the classroom — they’re learning through doing, tackling real research problems and running experiments on one of Australia’s most powerful supercomputers.
This year’s program brings together a diverse set of projects, each led by an experienced project lead, spanning fields from quantum computing and artificial intelligence to astronomy, materials science, and health research. For the interns, it’s an opportunity to work on projects that not only push scientific boundaries but also give them hands-on access to the computational resources that they would rarely encounter as students. The final showcase provides the interns with an opportunity to share their findings.
Exploring the Diversity of Research
The Pawsey Summer Internship Program is designed to immerse students in the full research experience. Across the program, interns are engaging with projects in four main areas: AI and machine learning, quantum computing and advanced computing, materials science and molecular modelling, and astronomy and supercomputing.
AI and Machine Learning Projects
Several projects this year focus on using artificial intelligence to tackle real-world challenges. In LLM-Driven Computational Chemistry Workflow Automation, interns will build a prototype system that uses large language models to automate complex chemistry calculations, from generating input files to analysing results. Another project, Next Generation Grid Modelling with AI-Powered Energy Data, tasks interns with developing synthetic data models of human activity to improve energy demand forecasting for smart cities. In Prediction of Transdiagnostic Markers of Mental Health via Passively Sensed Data, interns will work with smartphone data and machine learning to detect and predict mental health patterns, gaining experience in data science and behavioural health analysis.
Quantum Computing and Advanced Computing Projects
Quantum computing offers a radically new way of approaching computation, and this year’s interns will work at the forefront of this emerging field. In Quantum Machine Learning, students will explore quantum algorithms and run benchmarks on both classical and quantum hardware. The Quantum Image and Signal Processing project offers interns the chance to explore how quantum techniques can improve the speed and efficiency of image and signal analysis for fields such as medical imaging and satellite data.
Materials Science and Molecular Modelling Projects
Some projects focus on understanding processes at the molecular and atomic scale. In Modelling Nucleation of Biominerals, interns will use molecular dynamics simulations to explore how biomolecules influence mineral formation, with applications ranging from coral reef conservation to biomimetic material design. In Computational Investigation of the Stability of Calcite Micropillars, students will model how certain organic molecules stabilise microscopic mineral structures, contributing to knowledge in materials science and geochemistry.
Astronomy and Supercomputing Projects
Astronomy research offers a window into the universe, and Pawsey’s HPC systems make it possible to tackle some of the largest data challenges in the field. In Optimising Memory Management and GPU Kernel Execution within the BLINK Radio Astronomy Imaging Pipeline, interns will work to make high-speed imaging of the radio sky even more efficient. In Optimising Research Data Workflows with Object Storage Environments, students will investigate how researchers interact with large-scale data storage systems, finding ways to make data transfer faster and more reliable.
Learning Through Doing
For Pawsey interns, this program is more than a project — it’s an immersive learning experience. They aren’t observers; they’re contributors. Interns are involved in every stage of their projects, from exploring code and running simulations to testing results and developing tools. Along the way, they gain direct experience in high-performance computing, specialised software, and cutting-edge research methods. In addition to technical skills, the interns develop valuable professional skills from science communication and presenting results to teamwork and collaboration.
Crucially, Pawsey gives interns access to supercomputers such as Setonix, a resource few students encounter. This allows them to work on problems at scales far beyond what a typical university lab can offer, giving them both practical skills and a deeper understanding of how computational science drives research today. This year, Pawsey has allocated 2.250 million hours of CPU and GPU hours to intern projects.
A Glimpse into the Future
As this year’s internships get underway, Pawsey continues to demonstrate how powerful opportunities for hands-on learning can be. For the interns involved, it’s a summer of discovery — one that could shape their academic paths and future careers.
For Pawsey, it’s about fostering a new generation of researchers who are confident working with advanced computational tools and tackling challenges across a variety of fields. While this program is now underway, it also offers a glimpse of what’s possible for future interns — a chance to explore, innovate, and contribute to the frontier of science.
From exploring the quantum realm to modelling the complexities of human health, Pawsey’s summer interns are making their mark on some of science’s biggest challenges. It’s a testament to Pawsey’s mission: to open the doors of high-performance computing to the next generation of researchers, and to empower them to shape the future.
This Year’s Internship Projects:
- Optimising Research Data Workflows with Object Storage — Improving data transfer and storage workflows for big science projects.
- Optimising Memory Management in the BLINK Radio Astronomy Imaging Pipeline — Making high-speed radio astronomy imaging faster and more efficient.
- Quantum Machine Learning — Exploring quantum algorithms and benchmarking performance on classical and quantum systems.
- Quantum Image and Signal Processing — Investigating quantum techniques to improve image and signal analysis.
- LLM-Driven Computational Chemistry Workflow Automation — Building AI systems to automate complex chemistry calculations.
- Next Generation Grid Modelling with AI-Powered Energy Data — Developing synthetic data models to improve energy demand forecasting.
- Prediction of Transdiagnostic Markers of Mental Health via Passively Sensed Data — Using AI to detect and predict mental health indicators from smartphone data.
- Modelling Nucleation of Biominerals — Simulating how biomolecules influence mineral formation.
- Computational Investigation of the Stability of Calcite Micropillars — Modelling microscopic mineral structure formation and stability.
