NoiseNet

Job Description

We're offering two paid Summer Engineering Internship opportunities for current university students who want to apply what they're learning to real-world engineering challenges.

You'll work alongside our technical and operations teams on projects involving real-world noise data, analysis and modelling. Rather than hypothetical university exercises, you'll contribute to projects designed to solve real problems for NoiseNet and our customers.

This is an opportunity to build your technical skills, gain experience in a growing Australian technology startup and see how engineering, data and technology come together in a commercial environment.

• Location: Sydney or Brisbane
• Internship period: December 2026 to February 2027, exact duration dependent on candidate availability

Project Details

We’re accepting expressions of interest on the following projects:

“Machine learning techniques for dog bark diarisation”

• Develop machine learning approaches for identifying one individual dog’s bark from another.
• Collate, clean and prepare real-world noise data for analysis.
• Investigate and implement machine learning techniques such as feature extraction, embedding models, clustering etc.

“Sound classification techniques for environmental noise”

• Build on established machine learning workflows for the classification of environmental noise
• Train and verify deep learning models

“Extraction of contextual acoustic meta-information from images”

• Propose and develop techniques for extracting contextual information about the acoustic environment from photographs, imagery and text.

“Experiments in sound localisation”

• Investigate the effects of microphone array parameters on sound localisation performance.

In all cases, you will be working with our engineering team and be expected to:

• Document your work, findings and recommendations.
• Contribute ideas and new approaches to how we handle, analyse and present noise data.
• Maintain excellent communication with your supervisor and colleagues throughout the project.
• Present the outcomes of your project at the conclusion of your internship.

Qualifications

• Currently studying Mechanical, Mechatronics, Electrical, Software, or another relevant engineering degree
• Comfortable working with data and interested in using it to solve practical problems
• Experienced with some programming or data analysis through university coursework, personal projects or work experience, using tools such as Python, MATLAB or R
• Diligent and reliable, with good attention to detail and a preference for producing accurate work
• Comfortable working autonomously, taking ownership and figuring out problems as you go
• A clear and confident communicator who can collaborate with people across technical and non-technical teams

Nice to have

• You have an interest in sound, audio, acoustics, signal processing or IoT technology
• You have experience with Python, Jupyter Notebooks, Linux or other programming/software development tools
• You've worked on university or personal projects involving data analysis, modelling, machine learning or signal processing
• You will be completing your studies during 2027