AARNet (APR – 3037)

Building Trustworthy AI for Network Analytics

Engineering, IT, Mathematics and Statistics

ABOUT THE INDUSTRY PARTNER

The Australian Academic and Research Network (AARNet) is Australia’s national research and education network, connecting universities, research institutions, schools, hospitals and cultural organisations through high-performance digital infrastructure.

AARNet delivers advanced networking, cyber security and digital services that enable world-leading research and collaboration across Australia. Working alongside experienced engineers and technology specialists, you’ll contribute to innovative projects that improve the reliability, resilience and performance of one of Australia’s most important research networks.

WHAT’S IN IT FOR YOU?

This internship provides an opportunity to explore emerging analytical techniques that help transform complex network data into trustworthy, explainable insights for real-world decision-making.

During the internship, you’ll have the opportunity to: 

• Investigate cutting-edge AI techniques, including machine learning, large language models and mathematical reasoning.
• Explore how large language models can provide natural-language access to complex operational data and complement traditional machine learning approaches. 
• Apply your research to large operational datasets. 
• Work alongside experienced engineers and subject matter experts. 
• Compare analytical approaches and evaluate their strengths and limitations. 
• Contribute to the development of more transparent and trustworthy AI systems. 

RESEARCH TO BE CONDUCTED

AARNet is currently undertaking projects to make its digital data more accessible for analysis. One recent stream of work has focused on combining large language models (LLMs) with data query tools to analyse operational network data. While this work has highlighted the value of LLMs, it has also shown that they do not always produce analysis that can be verified and trusted by a human operator. 

This internship will investigate analytical techniques that are more amenable to human validation, exploring approaches ranging from classical machine learning (ML) and deep neural networks to satisfiability modulo theories (SMT) solvers, theorem provers and other advanced analytical techniques. 

Working collaboratively with AARNet supervisors, the successful applicant will investigate how these approaches could be applied to operational challenges such as cost optimisation of network construction, network fault rectification, predictive fault detection, maintenance and workflow throughput optimisation. The project offers flexibility for the successful applicant to help shape both the analytical approach and its application while working closely with engineers and subject matter experts. 

SKILLS WISH LIST

If you’re a postgraduate research student and meet some or all the below we want to hear from you. We strongly encourage women, indigenous and disadvantaged candidates to apply:

Essential 

• Strong background in mathematics, statistics, computer science or a related discipline. 
• Strong analytical and critical thinking skills. 
• Interest in artificial intelligence, machine learning or advanced computational methods. 
• Ability to communicate complex concepts to both technical and non-technical audiences. 

Desirable 

• Explainable AI. 
• Formal methods. 
• Logic or theorem proving. 
• Optimisation techniques. 
• Machine learning. 
• Python or similar programming languages.

RESEARCH OUTCOMES

By the end of the internship, you will have contributed towards: 

• Evaluation of analytical techniques suitable for complex network data. 
• Recommendations for improving the transparency and trustworthiness of AI-assisted analysis. 
• Prototype analytical workflows or proof-of-concept tools. 
• Insights to support future AI adoption within network operations. 

ADDITIONAL DETAILS

The intern will receive $3,300 per month of the internship, usually in the form of scholarship payments.

It is expected that the intern will primarily undertake this research project during regular business hours and maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.

The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.

Please note, applications are reviewed regularly and this internship may be filled prior to the advertised closing date if a suitable applicant is identified. Early submissions are encouraged.

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