QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.
Found 3 matching student topics
Displaying 1–3 of 3 results
Interactive (and collaborative) robot programming using language (Project 2.5 - Joint CSIRO/ACC)
Programming robots to carry out desired tasks is difficult and time-consuming. This PhD project focuses on collaborative and instructional dialogue agents to help human operators program robot tasks.In this collaborative scenario, a human operator converses with an AI agent to explain the steps that are to be performed, using high-level references and abstractions that make sense to the human, as opposed to simple verbal instructions corresponding to rudimentary robot movements. The AI agent must interpret the high-level instructions and translate …
- Study level
- PhD
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Design
- Research centre(s)
-
Design Lab
Supporting Boundary Management in Young People’s AI Companion Interactions
We invite applications for a PhD position within an interdisciplinary research team examining how young people engage with AI companion chatbots (e.g., Character.AI, Replika) and how they manage risks and boundaries in these interactions.AI companions have grown in popularity since the COVID-19 pandemic and are increasingly used by young people seeking connection. This trend raises important concerns, including exposure to harassment, misinformation, and self-harm, as well as the potential impact on human relationships when reliance on AI companions becomes significant.This …
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Computer Science
Enhancing 3D visual understanding through multimodal data fusion
The demand for 3D scene understanding through point clouds is rapidly growing in diverse applications, including augmented and virtual reality, autonomous driving, robotics, and environment monitoring. However, the field faces challenges due to limited data availability and predefined categories. Training deep 3D networks effectively for sparse LiDAR point clouds requires significant amounts of annotated data, which is both time-consuming and expensive. Building on the advancements in 2D models that leverage the power of image and language knowledge, our project aims …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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