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 18 matching student topics
Displaying 1–12 of 18 results
SLAM inside the human body: camera tracking and 3D reconstruction for medical procedures
Minimally invasive surgery and endoscopic interventions rely heavily on the clinician’s ability to understand and navigate complex internal anatomy using only a narrow and often restrictive field of view. Having access to an accurate and dynamic 3D reconstruction of the endoscopic scene, together with reliable camera pose estimation can significantly improve spatial awareness and navigation during procedures. The generated map can be used alongside the device’s estimated location to help clinicians better orient themselves within the patient, and it also …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
3D scene reconstruction for medical application
New computer vision methods using machine learning can reconstruct 3D dynamic environments. We are working on medical application to track clinicians, patients body, lesions and tools. Those techniques can be applied for tracking injuries (e.g. wound), providing analytic of operating theatre, and provide guidance for surgical intervention.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Scene Understanding for Underwater Imagery
Underwater ecosystems, including coral reefs and seagrass meadows, play a critical role in maintaining marine biodiversity, providing coastal protection, and supporting fisheries and tourism economies that millions depend upon globally. These habitats are increasingly vulnerable to climate change, pollution, and other anthropogenic impacts, demanding urgent efforts to monitor and restore them. Accurate scene understanding of underwater imagery enables fine-scale ecosystem monitoring across spatial and temporal scales, supporting essential activities such as habitat and biodiversity assessment, validation of aerial and remotely …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
- Research centre(s)
- Centre for Robotics
Quantum machine learning
Quantum machine learning is the integration of quantum algorithms within machine learning programs with great potential to solve complex problems. For instance, Google’s Sycamore processor (61) performs in 200 seconds a task that would require 10,000 years using a classical computer.
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Adaptive and efficient robot positioning
I am looking for highly motivated and talented PhD students to work with us on robot localisation and navigation. The students would join my DECRA Fellowship project "Adaptive and Efficient Robot Positioning Through Model and Task Fusion" funded by the Australian Research Council, which provides substantial top-up scholarships in addition to QUT's tax-free base stipend.Robot positioningWhere are you? This is a fundamental question to which most of us usually know the answer. And so do the birds singing in our …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
- Research centre(s)
- Centre for Robotics
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
Re-localisation in natural environments
Re-localisation in robotics involves the process of determining a robot's current pose, consisting of its position and orientation. This can either be within a previously mapped and known environment (i.e. prior map) or relative to another robot in a multi-agent setup. Re-localisation is essential for enabling robots to perform tasks such as autonomous monitoring and exploration seamlessly, even when they encounter temporary challenges in precisely tracking their location in GPS-degraded environments. For instance, consider the 'wake-up' problem, where a robot …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Advancing monitoring of diverse grass pollen with computer vision
We're seeking motivated students to join an ongoing multidisciplinary project that brings together computer vision and deep learning field with pollen aerobiology as part of the Australian Research Council Discovery Program—Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources. The successful candidate will be primarily based in the Allergy Research Group at QUT's Kelvin Grove campus.Grass pollen is the main outdoor allergen source globally, triggering hayfever and asthma in over 500 million people. With over 10,000 species, the influence of …
- Study level
- PhD
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
- Centre for Robotics
Centre for Immunology and Infection Control
Investigating the application of sustainable AI practices in construction
The construction industry plays a vital role in the global economy and there is a growing interest in utilising artificial intelligence (AI) to improve its productivity and efficiency. Despite the industry's significant contribution to the economy, it has faced challenges such as large cost overruns, extended schedules, and quality concerns. Nevertheless, AI is making significant strides to remove these issues by revolutionising various aspects of the construction industry. This is evident from enhancing project planning and design to improving construction …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
Physics-informed machine learning
Recent advances in computer vision have demonstrated superhuman performance on a variety of visual tasks including image classification, object detection, human pose estimation and human analysis. However, current approaches for achieving these results center around models that purely learn from large-scale datasets with highly complex neural network architectures. Despite the impressive performance, pure data-driven models usually lack robustness, interpretability, and adherence to physical constraints or commonsense reasoning.As in the real world, the visual world of computer vision is governed by …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Sport AI
Videos of sport activities are widely available at large scales. AI and its sub-fields, especially computer vision and machine learning, have a great potential to analyse, understand and extract useful information from these videos.This project aims at using AI and its subfields in computer vision and machine learning to develop techniques for analysing sport videos to extract intelligence for players and coaches.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Drone and satellite Artificial Intelligence
Satellite and drone/UAV data has a great potential to provide large-scale analytics for many domain applications. However, the wide range of data of diverse nature (e.g., optical vs. SAR, high-resolution vs. wide-coverage, mono- vs. hyper-spectral, 2-D vs. 3-D) also poses significant challenges for analytics.Deep learning holds great promise to deal with these tasks. While the number of research in this area is increasing, there still exists challenges such as co-learning of multimodal data, limited data annotation, and uncertainty in the …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Contact us
If you have questions about the best options for you, the application process, your research topic, finding a supervisor or anything else, get in touch with us today.