Honours and Masters project

This is a Winter Student Research Internship. Please apply here: https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter

Advances in artificial intelligence (AI) are reshaping work across computing-related professions. Although AI systems can perform some technical and cognitive tasks, they continue to rely on human judgement, responsibility, and contextual understanding. Identifying where human contribution remains critical is therefore a substantive question for disciplines concerned with socio-technical systems.

This is a Winter Student Research Internship ONLY not an honours or minor thesis project at this time.

Please apply here if you are interested in the role before the deadline: 

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

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This project explores how emerging quantum optimisation techniques can be applied to sustainable energy management problems such as electric vehicle (EV) charging coordination and smart energy scheduling. The research focuses on small-scale simulated optimisation problems related to renewable energy systems and the “duck curve” challenge in modern electricity grids.

Pharmacists handle thousands of medications daily, requiring constant verification of prescriptions, allergy checks, and inventory management across multiple digital and physical systems. This fragmented workflow leads to frequent context switching, high cognitive load, and increased risk of workflow inefficiencies and medication errors. 

This winter internship focuses on applying visual analytics to support multi-objective decision-making in wind farm planning under biodiversity constraints.

This project addresses the language ignoring problem in embodied AI, where robots learn visual shortcuts instead of following instructions. Building on our preprint establishing the relationship between planning width (instruction granularity) and learning difficulty, you will develop WALR—a reward design framework that adapts to instruction complexity. WALR scales language grounding rewards based on instruction granularity (coarse vs.

This project develops PACE-Drone, an intelligent drone planning system that learns from experience rather than following pre-programmed scripts. Unlike current drones that treat each mission independently, PACE-Drone maintains a persistent belief over user preferences via Bayesian learning, actively discovers implicit constraints from historical mission logs, and balances exploration with task completion based on instruction granularity.

Our research explores novel map representations and projections.

This project seeks to design and trial new map representations for seeing Australian population data sets in new and ideally more effective ways. 

Why is this needed? 

DeFi (Decentralised Finance) [Link] provides financial instruments and services through smart contracts on block chain systems. Comparing to traditional finance systems, DeFi eliminates the need of brokerages, exchanges, or banks as intermediates, and allows users to perform various financial activities including lend/borrow funds, trade cryptocurrencies, and earn interests. 

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.