It is said that ChatGPT is ‘not trying to be right, it’s just trying to be plausible.’ While the LLM community talk about hallucination, it is a complex phenomenon and a product of their construction. The training and theory of LLMs has no notion of truth, they generate text with no critical evaluation of content or sources. Many text sources are opinions, some may have subtle or not so subtle propaganda, some reflecting misinformed views, and LLMs reproduce this mess. Moreover, the training and theory of LLMs has no notion of epistemic uncertainty, they have no sense of uncertainty. …
Honours and Masters project
Displaying 1 - 10 of 280 honours projects.
Impact Stories from HCC
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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Winter Student Research Internship: Co‑designing Teamwork Feedback for Computing Education
This is a Winter Student Research Internship. Please apply here: https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter
Team‑based projects are widely used across computing education to support the development of technical competence alongside collaboration and professional skills. While students engage extensively in teamwork during these projects, educators often face challenges in seeing and responding to teamwork processes as they unfold, which can constrain opportunities to provide timely, process‑focused feedback beyond final project outcomes.
Winter Student Research Internship: Professional Value Beyond Automation: Graduates with Human-Centred Computing Background in AI-Mediated Work
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.
Pupil Labs eye tracking for visualisation experimentation
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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Quantum Optimisation for Human-Centred Sustainable Energy Applications
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.
SafePhARm: Safe and Efficient Pharmacy Practice Through Augmented Reality
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.
Winter Research Project - Visual Analytics for Bir-Sensitive Wind Farm Planning
This winter internship focuses on applying visual analytics to support multi-objective decision-making in wind farm planning under biodiversity constraints.
WALR — Width-Aware Language Reward for Vision-Language-Action Models
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.
PACE-Drone — Preference-Aware Continual Exploration for Active Drone Planning
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.