INTERNSHIP DETAILS

Summer Engineering Interns

CompanyNoiseNet
LocationCity of Brisbane
Work ModeOn Site
PostedOctober 8, 2026
Internship Information
Core Responsibilities
Interns will work alongside technical teams on real-world engineering projects involving noise data analysis, modelling, and machine learning. They are expected to document findings, contribute new approaches, and present project outcomes at the end of the internship.
Internship Type
intern
Company Size
11
Visa Sponsorship
No
Language
English
Working Hours
40 hours
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About The Company
NoiseNet specialises in low cost electronic monitoring technology with IoT (Internet of Things) capabilities to gather data for the capture and analysis of noise. Our unique, innovative solution resolves noise complaints for residents and regulators through an automated, AI-driven, objective process.
About the Role

Company Description

Join NoiseNet as a Summer Engineering Intern and spend your university break working on real engineering problems that help create happier, healthier and more tranquil communities. 

Think back to a restless night when noise kept you awake — a barking dog, a blaring car alarm or pounding music. Now imagine that same night repeating over and over. Noise is more than an annoyance; it can have a real impact on people's health and wellbeing and is a common source of complaints to local councils. 

At NoiseNet, we're on a mission to change that. We're making the management of noise pollution easier using IoT technology and Artificial Intelligence-driven smart noise monitoring. 

Our technology gives noise regulators access to 24/7 monitoring and intelligent analysis, helping them investigate and resolve noise issues faster, more reliably and at a lower cost. 

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 

Additional Information

About NoiseNet 

NoiseNet is an Australian seed-funded technology startup specialising in affordable, IoT-enabled noise monitoring technology. 

Our AI-driven solution helps organisations monitor, understand and respond to noise issues quickly and objectively. With customers in Australia, Singapore, Canada and the United States, we're using technology to make noise management smarter, easier and more effective. 

Learn more about us at www.noisenet.com. 

How to apply 

Please submit your CV via the application form and tell us a little about your interest in the internship. Candidates will be required to complete a background check as part of our hiring process. 

We welcome applications from people of all backgrounds, abilities and identities. We value diversity and believe different perspectives strengthen our creativity, innovation and overall success. We are committed to creating an inclusive workplace where everyone can thrive. 

Key Skills
PythonMATLABRMachine LearningData AnalysisSignal ProcessingIoT TechnologyAcousticsDeep LearningSound LocalisationFeature ExtractionClusteringLinuxJupyter NotebooksModellingEngineering
Categories
EngineeringTechnologyData & AnalyticsSoftwareEnvironmental & Sustainability