Honours and Masters project

This is a Summer Student Research Internship advert, running for 10-12 weeks over the summer break.

There is scope for this work to continue as an honours or minor thesis project afterwards. If you are interested in that, particularly the immersive visualisation side, please get in touch.

(Note that summer and winter student internships must be applied for here: https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter)

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This project develops a distributed digital twin framework for smart grid data monitoring and analytics. The research focuses on simulating and emulating smart grid environments to enable real-time monitoring, intelligent data processing, and predictive analytics. By integrating distributed computing, digital twin technology, and adaptive resource management, the framework aims to improve the scalability, reliability, and efficiency of smart grid operations while supporting data-driven decision-making and proactive system maintenance.

This project aims to develop a lightweight YOLO-powered digital twin framework for real-time air traffic monitoring using airport surveillance cameras. The research investigates how computer vision techniques can be integrated into a digital twin capable of representing aircraft locations, movement trajectories and traffic flow in near real time.

LearnLens is an inclusive adaptive agentic AI learning system designed to support students while they learn complex and unfamiliar concepts. Its central purpose is to measure and support how learning develops, rather than evaluating students mainly through a final answer, program, circuit, assessment submission, or completed product.

This project asks how quantum and hybrid quantum-classical optimisation can be used not just to solve energy management problems more efficiently, but to solve them in ways that are fair, transparent, and usable by real people. The student will build simplified sustainable energy scheduling and resource allocation models as QUBO problems, then solve them with classical and quantum-inspired methods using Qiskit.

Motivation: The Unseen Bedrock of Modern Life

Cybersecurity and cryptography are the invisible, essential foundations of the modern digital world. Every day, billions of transactions and communications rely on complex mathematical puzzles to ensure confidentiality, integrity, authenticity, privacy and even more security features.

Congenital heart disease affects 1 in 100 babies. Spatial gene expression patterns are critical to understand how the heart develops and what underlying genetic patterns are behind heart malformation. High-throughput spatial temporal data have been recently generated with spatial transcriptomics technologies. Capitalising on these rich datasets, we aim to build a custom analysis workflow in which the cells are profiled with precise spatial gene expression information. The student will provide fundamental contribution to of this project, by:

This project aims to develop an explainable epidemiology system that combines generative AI, biostatistics, and epidemiological reasoning to support healthcare analysis and decision-making. The system will take structured health data as input, analyse it multimodally, and build a knowledge base that can be queried by epidemiologists and health researchers.

Discussion of dangers of artificial intelligence (or artificial superintelligence, ASI) being an ambitious subordinate and ultimately taking over control from humans goes back at least as far as R J Solomonoff (1967).  R J Solomonoff (1985) gives an approximate time-frame in which this might occur.

Future electricity networks are becoming increasingly complex due to the rapid growth of distributed energy resources (DERs), batteries, renewable generation and intelligent infrastructure. Although modern networks collect large volumes of operational, asset and maintenance data, this information is often distributed across multiple systems, making it difficult for engineers and operators to understand the true reliability and resilience of the network or identify emerging risks before failures occur.