Honours and Masters project

Bayesian network (BN) inference—computing posterior probabilities given evidence—is a core task in probabilistic reasoning, but it becomes computationally expensive as networks grow in size or treewidth increases. Quantum-accelerated BN inference explores whether quantum algorithms and quantum circuit representations can provide practical advantages for approximate inference and sampling, while still making realistic assumptions about data access, noise, and limited quantum resources.

Quantum-accelerated Bayesian network (BN) structure learning asks whether quantum algorithms can speed up the combinatorial search over directed acyclic graphs while still making realistic systems assumptions.

Feedback is central to student learning, but feedback does not automatically lead to improvement. Students need opportunities to understand, evaluate, and act on feedback, while teachers and teaching teams need to design feedback practices that are clear, actionable, timely, and connected to learning activities.

Background and motivation

Cyber Threat Intelligence (CTI) plays a vital role in today's cybersecurity landscape by collecting and analysing data about current and potential threats, providing insights to better understand, mitigate and respond in this ever-evolving environment. A core component of CTI is the identification of adversarial Tactics, Techniques, and Procedures (TTPs), which describe how attackers operate at a strategic and operational level.

This is a Winter Student Research Internship 2026 advert (and already filled).

However it will convert to honours /minor thesis project after the break. If you are interested in this research as a thesis particularly the 3D component, please contact me. 

(Note that Winter and summer student internships must be  applied for here:

https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter)

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Background:

Please note that this Honours and Masters Project  topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Can a Transformer understand a software patch and predict whether it truly fixes a vulnerability, introduces a new weakness, or leaves the system still exploitable?

This is much more specific than normal vulnerability detection.

Instead of asking:

“Is this code vulnerable?”

we ask:

Please note that this Honours and Masters Project topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

This project is not just another IDS project. It sits at the intersection of four powerful areas:

So you will deal with:

Despite an increase in the usage of AI models in various domains, the reasoning behind the decisions of complex models may remain unclear to the end-user. Understanding why a model entails specific conclusions is crucial in many domains. A natural example of this need for explainability can be drawn from the use of a medical diagnostic system, where it combines patient history, symptoms and test results in a sophisticated way, estimate the probability that a patient has cancer, and give probabilistic prognoses for different treatment options.

Embodied intelligence represents a rapidly emerging paradigm in robotics where intelligent behaviour arises through the integration of perception, decision-making, action, and interaction within a physical agent operating in the real world. Advances in autonomous robotics, computer vision, sensor fusion, and human-robot interaction have enabled mobile robotic systems to perform increasingly sophisticated tasks in dynamic environments.