Honours and Masters project

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.

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.

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.

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.

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 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.  

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.

Inclusive learning environments are essential for ensuring that all students can fully participate and succeed. Neurodivergent learners—including those with ADHD, autism, dyslexia, and other cognitive differences—often navigate university settings that were not designed with their learning profiles in mind. Challenges related to feedback interpretation, cognitive load, communication, and assessment design can create barriers that impact learning, confidence, and wellbeing.

This is one of our CSIRO Next Generation AI Graduates Honors projects:

https://www.monash.edu/it/ssc/raise/projects 

Note: You Must be a Domestic Student i.e. Australian or New Zealand Citizen or Australian Permanent Resident  

Project Description

Multiple master thesis/honour projects on MLLM/ human understanding are available.

Benefits: we aim for publication at top conferences and journals. You will have chance for full PhD scholarship. For those working hard, paid RA opportunities will also be provided.

requirement: WAM>80 and high self-motivation