Honours and Masters project

The research project aims to build:

- An Energy Market Simulator for Future Energy Systems (Smart Grid). 

Future energy systems are envisioned to be running decentrally with full automatic control, high proportion of renewable energy (e.g., wind & solar), and abundant storage facilities. With many types of renewable energy sources are weather and climate dependent, accurate simulators with good visualization and data analytic capabilities are needed for operators to control the grid.

We are seeking a motivated student to contribute to the development of a digital risk-stratification tool that predicts short-term seizure recurrence in emergency care. This project involves working with free-text ambulance and emergency department clinical notes to extract prognostic indicators and support the fine-tuning of Large Language Models (LLMs) for medical risk prediction. The student will gain hands-on experience in data preprocessing, model development, and evaluation using real-world clinical datasets from one of Australia’s leading epilepsy centres.

Autoregressive moving average (ARMA) models remain a competitive tool for forecasting low signal-to-noise ratio time series, due to their flexibility, low complexity and physical plausibility. They predict the next observation in a time series as a linear combination of a number of previous observations as well as a number of hidden (latent) random innovations. The AutoARIMA package remains a staple benchmarking tool against which forecasting techniques must be compared.

This project seeks to explore and trial new map morphing representations for seeing river water quality data sets more effectively over time and space. 

We are particularly focusing on the Melbourne and the region of Victoria, but expect the visualisation to be applicable to any geographical region.  

We explore novel map representations and projections.

This project seeks to explore and trial new map representations for seeing Australian population data sets more effectively.

Whilst Australia has a population of 28 million, nationally it is counted as of the least densely populated in the world due to its size and topography (large areas of semi-arid and desert geography). Yet Australia is one of the world's most urbanised countries with 89% of the population living in a handful of urban areas, mostly with 50KM of the coastline along the east and south coastline. 

Traditional marketing analytics rely on predictive models that estimate the probability of customer behaviours such as churn or purchase. However, these models identify customers who are likely to act, not those whose behaviour can be influenced by an intervention. Uplift modelling addresses this limitation by estimating the causal effect of a marketing intervention on individual customers, enabling firms to target those whose behaviour is expected to change as a result of treatment rather than those who would act regardless.

This project aims to design and evaluate a hybrid optimisation framework using Qiskit and complementary classical solvers to address complex urban transport optimisation challenges.

The research will benchmark quantum-assisted and classical optimisation methods in terms of accuracy, scalability, and computational efficiency, and explore how hybrid algorithms can improve routing, scheduling, and energy management in next-generation urban mobility systems.

The world’s energy markets are transforming, and more renewable energy is integrated into the electric energy market. The intermittent renewable supply leads to unexpected demand-supply mismatches and results in highly fluctuating energy prices. Energy arbitrage aims to strategically operate energy devices to leverage the temporal price spread to smooth out the price differences in the market, which also generates some revenue.

The rapid growth of electric vehicles (EVs) is transforming the transportation systems worldwide. Both EV fleets and private EVs are emerging as a cleaner and more sustainable component of urban mobility, forming an effective way to solve environmental problems and reduce commute costs in future smart cities. Due to the complex spatiotemporal behaviors of passengers and their travel patterns, the unmanaged electric charging demand from EVs may significantly impact the existing transportation and electrical power infrastructure.

Thanks to the widespread deployment of smart meters, high volumes of residential load data have been collected and made available to both consumers and utility companies. Smart meter data open up tremendous opportunities, and various analytical techniques have been developed to analyse smart meter data using machine learning. This project will provide a new angle toward energy data analytics and aims to discover the consumption patterns, lifestyle, and behavioural changes of consumers.