This position is offered through the ANU Computing Internship courses (COMP4820 / COMP8830).
Semester 2, 2026 applications open on Monday 18th May 2026 and close on Sunday 31st May 2026.
Company
Haizea Analytics specialises in providing rapid access to satellite data and derived services for managing natural resources, ecosystems, and natural hazards such as floods and fires. What sets Haizea apart from others is its capacity to develop customised services and tools that harness high-resolution Earth observation data in a highly responsive, scalable and cost-efficient way. This unique ability is achieved by combining many years of expertise in spatial environmental applications.
Project
Precipitation forecasting across Australia faces a fundamental challenge: ground-based radar networks cover only a fraction of the continent, leaving vast regions without reliable nowcasting capability. This internship tackles that problem by developing a machine learning system that leverages Himawari-8 geostationary satellite imagery to deliver precipitation nowcasts in regions where radar-derived Rainfields precipitation estimates are sparse or unavailable.
As an intern on this project, you will explore and evaluate deep learning architectures for spatiotemporal prediction, working with real multi-spectral satellite observations and radar-derived rainfall estimates. You will design and train models that learn to implicitly assimilate information across both data sources — capturing the relationship between satellite-observable atmospheric features and surface precipitation — and benchmark your results against the operational PySTEPS nowcasting system using industry-standard verification metrics. This is a hands-on research engineering role suited to someone interested to work at the intersection of machine learning and atmospheric science.
Required technical skills
Required:
- Python (proficient) — model development, data processing, experiment management
- PyTorch or TensorFlow — deep learning model implementation and training
- Practical experience building and training machine learning models
Preferred:
- Familiarity with convolutional neural networks and generative models (GANs)
- Basic understanding of data assimilation concepts or numerical weather prediction
- Linux/HPC environment experience for GPU-based model training
- Familiarity with version control (Git) and reproducible research practices
Required/preferred professional and other skills
Required:
- Strong written and verbal communication — the intern will be expected to document methods and present results clearly
- Self-directed and comfortable working independently on open-ended problems
- Ability to read and engage critically with recent scientific literature
Preferred:
- Exposure to remote sensing data (satellite imagery, radar data)
- Experience working in a research team or academic lab environment
Delivery Mode
In-Person
Student location
Canberra
Project’s Special Requirements/ Conditions
None
Type of internship
Paid internship - the student will be engaged as an employee
How to apply
Applications are invited from eligible students to apply for the Computing Internship courses COMP4820 or COMP8830. Eligibility details of COMP4820 / COMP8830 and further information about the Computing Internship can be found on the Computing Internship page.
Eligible students can apply through the Computing Internship application form which will be available via the Computing Internship page between Monday 18th May 2026 and close on Sunday 31st May 2026.
You can nominate multiple preferred Internship projects and host organisations through the one online application form.
Eligibility and room available in degree to undertake COMP4820/COMP8830 will be assessed at the time of application. If you do not meet the eligibility criteria or do not have room in your degree to fit COMP4820/COMP8830, your application will not be progressed.
Your application will require you to upload the following documents:
- an updated copy of your resume, and
- an expression of interest (limit 350 words) for each organisation you wish to apply to (organisations with multiple projects may only submit one expression of interest, so state clearly which project/s you wish to be considered for).