This proposal is aimed to validate a concept for linking cementitious grout UCS with rheological properties through preliminary lab testing. The expected outcome is to assist mining practitioners in developing an early understanding of the cementitious grout strength to assess whether there is a possibility for shortening the waiting period for grout curing and ultimately increase the mining productivity. The preliminary testing results would lead to a potential grant application from the mining industry in Australia.

Primary Academic Supervisor: Associate Professor Danqi Li
Email: danqil@uow.edu.au
Project Duration: 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project: Nil 

Very high-energy electron (VHEE) beams are a promising option for FLASH radiotherapy, but the linac’s vacuum exit foil produces bremsstrahlung photons that add unwanted dose at depth and outside the field. This project will use Monte Carlo simulation (Geant4) to measure how the yield, energy spectrum and angular distribution of bremsstrahlung change with foil material (e.g. beryllium, aluminium, titanium, Kapton) and thickness. The project will also look at how each foil affects the electron beam itself, through scattering and energy loss. The results will be used to recommend foil designs that keep the foil mechanically sound while producing as few photons as possible.

Primary Academic Supervisor: Dr James Cayley
Email: jcayley@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: Geant4 experience is desirable but not essential; some coding ability is essential, as Geant4 is written in C++; however, knowledge of any programming language will suffice.

Halbach arrays are a specialised geometric arrangement of permanent magnets that produce a strongly asymmetric magnetic field distribution. They have a wide range of applications, including energy generation, particle accelerators, medical imaging, and neutron focusing. This project will prototype, construct, and characterise a small linear Halbach array using inexpensive NdFeB magnets mounted within a 3D-printed frame. Initially, finite-element simulations will be performed using COMSOL to predict magnetic field profiles, which will subsequently be validated through experimental magnetometry measurements using a Hall sensor. This work will provide the foundation for a longer-term project aimed at developing axial Halbach arrays for the manipulation of low-energy particle trajectories, such as neutron focusing.

Primary Academic Supervisor: Dr David Cortie
Email: dcortie@uow.edu.au
Project Duration: 8 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: NIL 

This project aims to research and develop a virtual AI teacher that supports accessible and personalised learning through sign language interaction. The student will develop an interactive learning platform that integrates a large language model (LLM), a pedagogical agent, a learner model, visual explanations, and sign-language resources. The virtual AI teacher will adapt its teaching strategies based on learner responses and provide appropriate explanations, hints, and exercises to support understanding. The project will evaluate the proposed approach by comparing adaptive teaching with a non-adaptive AI teacher using simulated learner profiles and predefined learning scenarios. Expected outcomes include a working prototype, an experimental evaluation, and a research report identifying opportunities for further development and evaluation with learners and educators.

Primary Academic Supervisor: Professor Hoa Dam
Email:  hoa@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: Applicant must have excellent programming skills in both front-end and back-end; strong knowledge and experience working with and fine-tuning Large Language Models and computer vision models. They must also have strong problem-solving and research skills.

MagicPlate is a family of high-resolution radiation detectors developed at UOW's Centre for Medical Radiation Physics (CMRP). These detectors can investigate changes in radiation beams during respiratory gating, when medical linear accelerators synchronise treatment with patients' breathing cycles.
The student will work alongside CMRP researchers and clinical medical physicists from three collaborating hospitals, performing experimental radiation measurements and developing Python algorithms to analyse time-dependent beam characteristics. The project offers hands-on experience in semiconductor detectors, medical physics and scientific data analysis, with opportunities to present findings at a CMRP research meeting and contribute to a scientific publication.

Primary Academic Supervisor: Dr Ilia Filipev
Email: ifilipev@uow.edu.au
Co-supervisor: Distinguished Professor Anatoly Rozenfeld
Email: anatoly@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: Curiosity and a solid foundation in Python programming

Generalised Autoregressive Score (GAS) models provide a flexible framework for modelling time series with parameters that evolve according to the scaled likelihood score [1]. This project explores the theory and implementation of Bayesian inference for a univariate GAS model using the No-U-Turn Sampler (NUTS) in Stan. It will incorporate focused Bayesian prediction [2], using a proper scoring rule to construct a generalised posterior tailored to a forecasting objective, such as tail accuracy. Using simulated data and a real time-series application, the project will compare conventional and focused Bayesian forecasts, examining predictive accuracy, uncertainty and computational efficiency. The project develops practical skills in Bayesian computation and time-series forecasting, with potential for further postgraduate research. [1] Creal, D., Koopman, S. J., and Lucas, A. “Generalized autoregressive score models with applications.” Journal of Applied Econometrics, 28(5): 777–795, 2013.
[2] Loaiza-Maya, R., Martin, G. M., and Frazier, D. T. “Focused Bayesian prediction.” Journal of Applied Econometrics, 36(5): 517–543, 2021.

Primary Academic Supervisor: Dr David Gunawan
Email: dgunawan@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: Knowledge of third-year statistics (STAT332 or STAT304) and programming skills in Python, R, or MATLAB.

Moisture barriers are widely used in pavement systems to control moisture migration into expansive subgrade soils, thereby reducing ground swelling, shrinkage, and the associated pavement distress. This project aims to investigate the feasibility of using recycled waste materials, particularly scrap tyre rubber and recycled glass, as sustainable alternatives for conventional moisture barriers. A series of laboratory tests will be conducted to evaluate the flowability, setting time,  and segregation characteristics of these materials and their effectiveness in limiting moisture ingress. The outcomes of this study will contribute to the development of environmentally friendly pavement solutions while promoting the beneficial reuse of waste materials and supporting sustainable infrastructure practices.

Primary Academic Supervisor: Associate Professor Vinod Jayan Sylaja
Email: vinod@uow.edu.au
Co-supervisor: Dr Pabasara Wanniarachchige
Email: pabasara@uow.edu.au
Project Duration: 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project: NIL

This project will employ techno-economic analysis (TEA), material flow analysis (MFA), and life cycle assessment (LCA) to evaluate the economic viability, material recovery potential, and environmental performance of lithium-ion battery recycling in Australia. TEA will assess the costs and revenues of recycling pathways, while MFA will quantify end-of-life (EOL) battery flows and critical material recovery opportunities, and LCA will evaluate the environmental impacts of alternative recycling processes. The findings will inform the design of a sustainable reverse logistics network for the collection, transportation, and processing of EOL lithium-ion batteries, supporting the development of a viable domestic recycling industry in Australia.

Primary Academic Supervisor: Associate Professor Senevi Kiridena
Email: skiriden@uow.edu.au
Co-supervisor: Professor Hongtao Zhu
Email: hongtao@uow.edu.au
Project Duration: 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project: Familiarity with cost modelling and logistics systems

When wire, tape or 3D-printing filament is paid out from a spool, the line must stay taut, so the spool is braked. Our group has developed a magnetic torsion spring that resists rotation one way and runs free the other, with no rubbing parts or power supply, but its torque pulses twice per revolution. Does the pulse reach the line? The student will model the spool, line and spring in MATLAB or Python, measure the spring on our bench, and design a bench-scale unwind line for a follow-on research degree or thesis. Animated overview: https://claude.ai/artifact/RP35zZaY1p61XYP957dBfr

Primary Academic Supervisor: Dr Chin-Hsing Kuo
Email: chkuo@uow.edu.au
Project Duration: 8 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: NIL

This project will investigate secure, privacy-preserving methods for verifying tenancy and property ownership relationships on TenantVoice’s digital housing platform. The student will review existing approaches, design verification workflows and develop an AI-assisted proof of concept for document processing and data matching. Using synthetic or appropriately approved data, the project will evaluate verification accuracy and selected security controls for APIs, databases and restricted AI access. Outcomes will include a prototype, an evaluation report and practical recommendations to minimise personal data collection, reduce unauthorised access and support trustworthy property reviews. Live integrations will depend on provider approval.

Primary Academic Supervisor: Dr John Le
Email: johnle@uow.edu.au
Co-supervisor: Dr Thai Vu
Email: tienv@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: Basic programming skills and familiarity with databases and web APIs. An interest in AI, cybersecurity and privacy is desirable. Privacy and cybersecurity research experience is the candidate's advantage.

This project aims to develop vision-language AI models for analysing satellite imagery using natural language descriptions or example images. The system will detect objects, segment regions of interest, and generate analytical reports to support environmental monitoring, disaster response, and maritime surveillance. The project tasks will include satellite image collection and annotation, algorithm development, system implementation, and evaluation. We are seeking a student in Computer Engineering, Computer Science, Information Technology, or a related major. The student is expected to have a strong background in deep learning, proficiency in Python and AI/ML libraries (e.g., PyTorch), and an interest in pursuing postgraduate research in AI and computer vision.

Primary Academic Supervisor: Dr Thanh Le Hoang
Email: tlhoang@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: The student is expected to have a strong background in AI, deep learning, computer vision, Python programming, vision-language models, and satellite image processing.

Einstein’s general theory of relativity predicts that a difference in the gravitational potentials between two locations in a gravitational field would introduce a redshift or blueshift for a light ray travelling from one location to another. However, observations made in our solar system, such as the 21-cm radiation line from Taurus A near occultation by the Sun, the Pioneer spacecraft experiments and the solar limb effect, clearly demonstrate that there exist anomalous redshifts which could not be explained by current theories and models.  We have developed a theoretical model and shown that besides the well-known Einstein gravitational redshift, which has been verified by various observations, there exists an extra gravitational redshift which is caused by the change of photon’s momentum produced in a curved spacetime. The aim of this project is to utilise the publicly available astronomical observation data to further test the theoretical model and to generate more publications.

Primary Academic Supervisor:  Associate Professor Enbang Li
Email: enbang@uow.edu.au
Project Duration: 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project: Physics students with interests in astrophysics

Web applications rely on security configurations to protect users, yet routine updates can inadvertently weaken these protections. This project will develop and evaluate an automated approach to detecting security regressions affecting script execution and page embedding. The student will create controlled applications, introduce realistic configuration changes, and compare configuration analysis with browser-based testing across two browsers. The evaluation will assess how accurately each approach identifies weakened protections while preserving legitimate functionality. Expected outcomes include a reusable testing tool, a benchmark dataset, and practical guidance to help developers maintain effective security protections as applications evolve.

Primary Academic Supervisor: Dr Nan Li
Email: nanl@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: Basic knowledge of HTML, JavaScript, and web development is required, along with familiarity with HTTP requests and responses.

This project aims to develop wearable sensors working with liquid metals. This project consists of major tasks: (1) Investigation of sensor capabilities of liquid metal-based composites; (b) design, analysis and optimisation of wearable sensors; and (c) experimental evaluation of dynamic performance of the sensors under various working scenarios.

Primary Academic Supervisor: Professor Weihua Li
Email: weihuali@uow.edu.au
Co-supervisor: Dr Liping Gong
Email: lgong@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: The candidate should have skills in the design and analysis of mechatronic systems.

Fully Homomorphic Encryption (FHE) is a transformative cryptographic technology that allows computations to be executed on encrypted data while preserving privacy. To lower the barrier to adoption, modern FHE compilers automatically translate conventional programs into FHE-compatible operations. However, these compiler frameworks are complex, and errors in program translation, numerical analysis or parameter selection can lead to incorrect results. This project aims to analyse and test existing open-source FHE compilers using automated techniques to uncover potential bugs and inconsistencies, with a focus on value-range analysis and parameter selection for programs containing loops. It will also investigate improvements to these analyses to support more reliable encrypted computation. The findings will contribute to the trustworthiness of FHE software ecosystems and support researchers who rely on these tools for secure computing, benchmarking, and experimental evaluation.

Primary Academic Supervisor: Dr Xueqiao Liu
Email: xueqiao@uow.edu.au
Co-supervisor: Dr Steven Duong
Email: hduong@uow.edu.au
Project duration:12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project: A background in computer science, software engineering, mathematics or related disciplines, with good programming skills in Python/C++ and an understanding of algorithms and cryptography. Experience with compiler analysis, software testing or homomorphic encryption is desirable. Familiarity with FHE tools such as HEIR, OpenFHE or Lattigo would be an advantage.

Experiments were carried out at Paul Sherrer Institute (PSI), Switzerland in a head and neck phantom using the SOI microdosimeter to compare in-house treatment planning system (TPS) with RaySearch TPS. LET optimised plans were prepared by FIONA TPS for a brain cancer treatment and the µ+ probe was used to verify the optimisation plan of proton therapy treatment. The summer project student will primarily focus on using Geant4 Monte Carlo simulations to model the complete experimental setup of the SOI microdosimeter developed by the Centre for Medical Radiation Physics within a head-and-neck anthropomorphic phantom. The student will simulate the microdosimeter's response to a proton treatment plan for a brain tumour and compare the simulation results with experimental measurements for validation. In addition, the student will gain practical experience with the RayStation treatment planning system.
The summer project will involve detector characterisation, calibration in the CMRP lab and perform Geant4 simulation. The student will contribute to paper preparation.

Primary Academic Supervisor: Dr Linh Tran
Email: tltran@uow.edu.au
Co-supervisor: Dr James Vohradsky
Email: jamesv@uow.edu.au
Project Duration: 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project: Recommended the student to be studying Medical Radiation Physics. Skill in Monte Carlo simulation will be preferred; hands-on experience working with detector characterisation in the lab

To address the issue of poor model robustness in student spatio-temporal behavior prediction caused by the interference of potential confounding factors (e.g., social environments and unexpected events), this project proposes a deconfounded spatio-temporal network prediction model. By integrating causal inference mechanisms, the research will identify and disentangle unobserved confounders within spatio-temporal trajectory data, thereby eliminating spurious correlations between features and behaviors. Ultimately, this project aims to achieve highly robust predictions of student behavioral trends in complex educational scenarios, providing scientific and reliable methodological support for personalised teaching interventions and campus safety management.

Primary Academic Supervisor: Dr Hui Luo
Email:  huil@uow.edu.au
Project Duration: 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project: Basic programming skills in at least one programming language (Python preferred) are required. Prior experience in machine learning is desirable but not essential. Students with a strong interest in learning machine learning and AI techniques are also encouraged to apply.

This project develops a goal-oriented decentralised multi-agent coordination platform for unmanned systems, such as UAVs, drones, and on-orbit servicing systems. The platform will allow autonomous agents to coordinate without a centralised controller, reducing the risk of system disruption caused by incomplete information and changing environmental conditions in mission-critical operations. These autonomous agents will learn to coordinate using Multi-Agent Reinforcement Learning (MARL) to achieve team goals in dynamic and complex environments. The platform will be integrated with Space ROS 2 (adopted by NASA) to demonstrate the deployment and scalability of the proposed platform in autonomous space and defence systems.

Primary Academic Supervisor: Dr Geeta Mahala
Email: gmahala@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: Experience using Python, Docker/Containers, Git/GitHub , Machine/Reinforcement Learning

Winter increases pressure on hospital beds, with demand varying across specialties. In partnership with Illawarra Shoalhaven Local Health District, this project will analyse aggregate hospital activity data to forecast winter bed requirements and support capacity planning. The student will investigate admissions, occupancy, length of stay and seasonal patterns; develop and compare forecasting approaches for selected specialties; and assess predictive accuracy and uncertainty. Over ten weeks, expected outputs are a reproducible analysis, a forecasting prototype, clear visualisations and a final report with planning recommendations. A dashboard prototype may be developed if time and data permit.

Primary Academic Supervisor: Dr Glen Wheeler
Email: glenw@uow.edu.au
Co-supervisor: Professor Sumeetpal Singh
Email: sumeetpals@uow.edu.au
Project Duration: 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project: Introductory university statistics and basic programming or data analysis skills. Familiarity with R or Python is desirable, together with an interest in healthcare applications.

This project aims to develop and evaluate offline programming approaches for industrial robotic automation. The student will investigate robotic simulation, motion planning, and automated toolpath generation using existing robotic platforms and software. Key activities include developing a virtual robotic workcell, implementing basic automated programming workflows, and evaluating robot movements, collision avoidance, and operational efficiency. The project will provide hands-on experience in robotics, automation, and digital manufacturing, with the goal of demonstrating a practical and efficient offline programming workflow for industrial applications.

Primary Academic Supervisor: Dr Lei Yuan
Email: leiy@uow.edu.au
Project Duration: 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project: NIL