Landslides are a major geohazard threatening the integrity of natural gas pipelines. Understanding how ground movement affects buried pipelines is critical for designing effective mitigation strategies and ensuring safe, reliable operation.
This project aims to:
1. Review existing research on landslide–pipeline interactions to identify knowledge gaps and best practices.
2. Develop a finite element modelling framework to simulate the interaction between landslide-induced ground movement and pipeline deformation.

The model will be used to study the influence of soil properties and landslide conditions, providing insights for risk assessment and mitigation in landslide-prone regions.

Primary Academic Supervising- Dr Chung Lu
Email- chenglu@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- N/A

This project aims to develop, analyse and implement mathematical models for pricing catastrophe bonds focusing on the derivation of partial differential equations (PDEs) and partial integro-differential equations (PIDEs), and exploring analytical and numerical solutions. The project will bridge financial mathematics (including Black-Scholes theory) with insurance-linked derivatives, introducing actuarial risk and stochastic modelling. 

Primary Academic Supervising- A/Prof. Mariano Rodrigo 
Email- rodrigom@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- As the project requires a strong foundation in financial mathematics and partial differential equations, a minimum mark of 90 in MATH317 and MATH305 is a prerequisite.

As additive manufacturing transitions from prototyping to producing functional parts, their long-term environmental durability remains a critical knowledge gap. This hands-on experimental project investigates the durability of high-performance 3D printed polymers, like carbon-fibre reinforced composites.

Supported by two PhD student mentors, you will fabricate specimens, subject them to accelerated weathering conditions such as UV radiation, and conduct mechanical tests (impact, hardness, creep, and fatigue) to quantify material degradation. Your findings will provide crucial data for predicting component service life and ensuring reliability in real-world applications, offering invaluable research experience.

Primary Academic Supervising- Dr Aziz Ahmed
Email- aziza@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- N/A

The ANSTO Australian Synchrotron is developing an existing linear accelerator into a user facility named Pulsed Energetic Electrons for Research (PEER). Experimental development has been conducted in collaboration with UOW. This has required the use of a Monte Carlo simulation, using the CERN Geant4 toolkit. The project has progressed to the stage where a more detailed simulation that accurately models the divergence of the beam is required. The student will work with CMRP researchers to improve the existing simulation, by creating their own custom codes to enhance the built-in functionality.

Primary Academic Supervising- Dr James Cayley
Email- jcayley@uow.edu.au
Co-Supervisor- Prof Mike Lerch
Email- mlerch@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Experience with Geant4 and knowledge of charged particle beams used for radiotherapy.

This undergraduate research project focuses on enhancing sustainable energy solutions through innovative machine and robot design using permanent magnets. Students will utilize advanced Finite Element Analysis (FEA) simulations and 3D-printed model verification to optimize magnetic field distributions associated with interacting mechanism motions. The project aims to develop novel designs for energy harvesting, conversion, and storage. This hands-on experience will deepen understanding of the engagement between machine design and electromagnetic principles while contributing to real-world clean energy challenges.

Primary Academic Supervising- Dr Chin-Hsing Kuo
Email- chkuo@uow.edu.au
Co-Supervisor- Dr Jon Roborts
Email- robertsj@uow.edu.au
Project Duration- 8 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- N/A

Liquid metals offer exceptional electrical conductivity, deformability, and biocompatibility, making them promising candidates for next-generation soft and wearable electronics. However, current patterning techniques suffer from complexity, low scalability, and limited resolution. This project proposes a novel, magnetically controlled liquid metal patterning method that integrates direct writing with oxide film strategies. The approach will simplify fabrication, improve line width consistency, enhance substrate versatility, and enable complex ultrathin flexible electronic architectures. Most importantly, the method aims to overcome limitations of existing processes and unlock practical applications of liquid metal electronics.

Primary Academic Supervising- Dr Hongda Lu
Email- hongdal@uow.edu.au
Co-Supervisor- Dr Weihua Li
Email- weihuali@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Strong experimental and analytical skills for design and development of intelligent systems.

Recent advances in Artificial Intelligence are transforming the way mathematics is explored and communicated. AlphaGeometry and similar AI tools recently made headlines by tackling and solving Olympiad level math problems. This project aims to experiment with and investigate the capabilities of using AI to generate mathematical content.

Primary Academic Supervising- Dr Joseph Tonien
Email- dong@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Students who apply for this project should have competent programming skills in Python and good math background. 

This summer project introduces students to the emerging field of soft robotics. Participants will design and prototype soft robotic actuators using flexible materials such as silicone. The project covers kinematic and dynamic modelling of soft structures using lumped-parameter methods, along with control implementation on microcontrollers (e.g., STM32, Raspberry Pi). Students will explore actuation systems, sensor integration, and both model-based and AI-driven control strategies. Emphasis is placed on hands-on fabrication, simulation, and testing. By the end, students will develop a functional soft robotic system and gain interdisciplinary experience in mechanical design, embedded electronics, and control systems.

Primary Academic Supervising- Dr Emre Sariyildiz
Email- emre@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- N/A

Knowledge Gaphs (KGs) capture human-curated factual knowledge as triplets (head, relation, tail), which together form a structured graph. Question Answering over KGs (KGQA) involves answering natural language questions by grounding reasoning in the information contained within the KG. Large Language Models (LLMs) have emerged as the state-of-the-art for QA tasks, owing to their strong natural language understanding capabilities. Meanwhile, Graph Neural Networks (GNNs) are widely applied to KGQA because of their ability to process and reason over complex graph structures. In this project, we aim to develop GNN-RAG, which integrates the language understanding strengths of LLMs with the reasoning power of GNNs in a retrieval-augmented generation (RAG) framework.

Primary Academic Supervising- Dr Shixun Huang
Email- shixunh@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Proficient in python and machine learning. Basic knowledge of graphs, NLPs, and graph neural networks.

Are you passionate about applying AI and machine learning techniques to create a tangible real-world impact? The overall goal of this research is to develop a portable device that enables vision-impaired individuals to travel independently and safely to their chosen destinations. This project aims to develop vision-language models for understanding traffic environments and generating step-by-step navigation instructions. The project tasks will include sensor data acquisition, data annotation, algorithm development, hardware and software implementation, and system evaluation.

We are seeking a student in Computer Engineering, Electrical Engineering, Computer Science, Information Technology, or a related discipline. The student is expected to have experience in Python/or MATLAB programming, digital electronics, GPU acceleration, and cloud computing. The student should also demonstrate a strong interest in pursuing postgraduate research in AI and machine learning.

Primary Academic Supervising- Prof Son Lam Phung
Email- phung@uow.edu.au
Co-Supervisor- Dr Hoang Thanh Le
Email- tlhoang@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- The student is expected to have experience in Python/MATLAB programming, digital electronics, GPU, and cloud computing. The student needs to have a strong interest in pursuing postgraduate.

Australia has an extensive network of paved roads spanning over 427,000 km. Monitoring and maintaining these road networks effectively and cost-effectively is a major challenge. This project aims to develop an AI-based smartphone app that can convert any citizen’s car into a road inspection vehicle. The app is designed to capture video of the road, process it in real time, and transmit the results to a cloud server via a 5G connection. It will detect and recognize a wide range of defects, record their precise GPS coordinates, and generate key statistics essential for planning road maintenance. We are seeking a student in Computer Engineering, Electrical Engineering, Computer Science, Information Technology, or a related major. The student is expected to have experience in Python/MATLAB programming, smartphone app development, GPU acceleration, and cloud computing. The student needs to have a strong interest in pursuing postgraduate research in AI and machine learning.

Primary Academic Supervising- Prof Son Lam Phung
Email- phung@uow.edu.au
Co-Supervisors- Dr Hoang Thanh Le & Prof Christian Ritz
Email- tlhoang@uow.edu.au & critz@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 mobile phone app development, image processing, AI, cloud computing experience.

Treatment of some cancers (e.g. glioblastoma multiforme) is very challenging. Radiosurgery with submillimetre X-ray beams, or Microbeam Radiation Therapy (MRT), is a novel approach to treat such cancers. This translational R&D program incorporates several potential projects, matched to compliment individual student interests, and include:
• MRT radiation detector design, simulation, development and testing, 
• Electronic readout hardware, firmware and software design development and testing,
• MRT treatment simulation, planning and validation 
• MRT image guidance and treatment enhancement using nanoparticles
• MRT related in vitro and in vivo preclinical experiments 
Projects may involve active participation in experiments at CMRP, National scientific accelerator facilities and clinical radiation oncology centres.

Primary Academic Supervising- Prof Michael Lerch
Email- mlerch@uow.edu.au
Co-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- Medical Physics or Physics.

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 assist with analysing the data obtained during this experiment and run Geant4 simulation to compare simulation results with experimental results.
Work will involve detector characterisation, calibration in the CMRP lab and perform Geant4 simulation. The student will contribute to paper preparation.

Primary Academic Supervising- 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 20 hours per week)
Prerequisites for the Project- Medical Radiation Physics and simulation skills (C++, Geant4) are preferred.

Designing high efficiency rectennas to wirelessly charge IoT devices deployed in healthcare systems for various applications such as medical equipment tracking and patient monitoring. These rectennas would harvest ambient RF energy from surrounding electronic systems, including medical IoT devices, Bluetooth instruments and wireless monitoring networks. However, when the harvested ambient energy is insufficient, the system can be supported by directed RF wireless power transfer, where dedicated transmitters (or power beacons) deliver energy directly to rectennas to ensure continuous and reliable operation. In addition (if time permits), AI can be integrated to optimise both the placement of devices and the scheduling of directed transmissions, thereby maximising power transfer efficiency across the healthcare IoT network and significantly reducing the need for battery replacement in critical devices.

Primary Academic Supervising- Dr Faisel Tubbal
Email- faisel@uow.edu.au
Co-Supervisor- Prof Raad Raad
Email- raad@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- It is preferred that student has completed Engineering Electromagnetics (ECTE213) to ensure adequate background in electromagnetic theory.

Phantoms approximating human body parts are used as medical trainers but tend to be prohibitively expensive, or do not provide effective feedback to the trainee and the educator. In this project, we will develop instrumented phantoms related to the CICO (Cant Intubate Cant Oxygenate) procedure using innovative 3D printing and inexpensive sensor technology to make them more accessible. This topic will be carried out in collaboration with the School of Nursing, and the Illawarra Health Education Centre at Wollongong Hospital, part of the Illawarra Shoalhaven Local Health District.

Primary Academic Supervising- Dr Manish Sreenivasa
Email- manishs@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Students should have strong aptitude for experimental work, signal processing, 3D printing, good programming experience as well as knowledge of physiology.

High fidelity medical trainers are state of the art mechatronic systems that can simulate patient behavior/conditions and help train medical and nursing students effectively. In this project, we will be further enhancing the SimMan 3G medical trainer to add functionalities that improve the realism and training scenarios. In particularly the focus will be on a hand prototype that can realistically reproduce capillary reflex as well as closed loop communications with the onboard controllers. This topic will be carried out in collaboration with the School of Nursing, and the Illawarra Health Education Centre at Wollongong Hospital, part of the Illawarra Shoalhaven Local Health District.

Primary Academic Supervising- Dr Manish Sreenivasa
Email- manishs@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Students should have strong aptitude for experimental work, signal processing, 3D printing, good programming experience as well as knowledge of physiology.

This project will develop an interactive prototype that shows how buildings can adjust their electricity use to support the grid, especially as renewable energy systems become more common. Students will work with a modular all-in-one system for instrumentation, automation and data collection. By linking this unit with computer-based grid simulations, they will create a hardware-in-the-loop setup to test and validate control strategies that shift power consumption and help integrate renewable energy. The prototype will give students practical experience in energy, control and automation, while acting as a learning tool to explain building-to-grid interactions to wider audiences.

Primary Academic Supervising- Dr Emily Yap
Email- eyap@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- The student should have experience in mechanical design, electrical circuitry, and electronic hardware control, and be open to learning a new programming language. They will have the opportunity to work alongside researchers at Innovation Campus and with an external collaborator.

The objective is to investigate the potential application of AI tools in the production, transportation and application phases of green hydrogen energy, covering the techniques and management perspectives. This project is a literature review-based study, covering five steps as follows:   
1.Search and select the relevant articles
2.Analyse and categorise the selected articles
3.Search and study the relevant and applicable AI tools
4.Read and extract the information from the selected articles with help of AI tools 
5.Draft literature review report, provide insights and implication for the next step in production and transportation of green hydrogen.

Primary Academic Supervising- A/Prof Tieling Zhang
Email- tieling@uow.edu.au
Project Duration- 8 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- N/A

This project aims to develop and characterize biomineralized collagen scaffolds with enhanced bioactivity for bone regeneration. We will analyze their nano- to micro-scale structure in detail, optimize mineral distribution, enhance mechanical strength, and evaluate the osteogenic potential. Understanding the biomineralization process at a nano-to micro-scale level will enable the design of optimized scaffolds that mimic native bone. This research addresses the critical need for effective bone repair solutions, advancing biomaterials science. The project will contribute to next-generation bone grafts, improving outcomes in orthopedic and maxillofacial surgery.

Primary Academic Supervising- Dr Xiao Liu
Email- xiaol@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- N/A

The Al/Ni multilayer system has attracted interest due to its structural, mechanical, magnetic, and corrosion resistance properties, and its ability to undergo a self-propagating exothermic reaction that enables local heat release for microelectronics joining without external furnaces. Conventional fabrication via sputtering produces nanoscale alternating layers with controlled thickness ratios but is costly and time-consuming. Recently, accumulative roll bonding (ARB) emerged as a low-cost alternative to produce nano-lamellar composites. However, plastic instability often causes Ni layer fragmentation and shear bands, reducing reaction continuity. This study explored Al/Ni multilayers using AA7075 and AA1050, examining microstructure, interface phases, and exothermic behaviour.

Primary Academic Supervising- Dr Lihong Su
Email- lihongsu@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- The student is expected to have the ability to operate a rolling mill and conduct metallographic testing.

This project will examine how harmonic power quality issues affect the lifetime of capacitors in switch mode power supplies. Laboratory experiments will accelerate aging under varied harmonic conditions to compare their influence on capacitor reliability. Building on earlier studies of stress from overvoltage, this work shifts the focus to distortion and its role in reducing lifespan. The outcomes are expected to identify which harmonic orders are most damaging and provide insights for designing more durable power electronic systems operating in modern, distortion-prone grids.

Primary Academic Supervising- Dr Obaidur Rahman
Email- orahman@uow.edu.au
Co-Supervisor- Dr Sean Elphick
Email- elpho@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- N/A

Join our research team at the SBRC to investigate how the temperature of building façades is measured using thermocouples, and to experimentally test how different thermocouple parameters affect measurement performance. Through this work, you will gain practical skills in experimental design, measurement analysis, and uncertainty evaluation, while contributing valuable insights into the reliability of one of the most widely used sensing technologies.

Primary Academic Supervising- Dr Steven Beltrame 
Email- sbeltrame@uow.edu.au
Co-Supervisor- Dr Alan Green
Email- alang@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Some background in heat transfer. An interest in experimental testing and data analysis is preferred.

Work with our research team to develop a prototype data logging platform tailored to building physics testing at the SBRC.  The successful candidate will explore different Internet of Things (IoT) hardware options to log data in our Building Insights Facility, and build a prototype data logger. 

Primary Academic Supervising- Dr Alan Green
Email- alang@uow.edu.au
Co-Supervisor- Dr Steven Beltrame 
Email- sbeltrame@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Previous experience with Arduino or other IoT platforms, and/or experience with electronics design is preferred.

The aim of the project is to develop a custom-built electrical characterisation system for organic x-ray sensors. This system will integrate hardware and software components to enable precise and efficient testing. Computer-Aided Design (CAD) tools will be employed to simulate and optimise key aspects of the system such as the optical components, sample stage, and laser alignment to ensure accuracy and reproducibility. In addition, the student will contribute to developing a user-friendly graphical interface that allows for alignment of the equipment, timing of the laser pulse and streamlined extraction of measurement data, enabling more effective analysis and interpretation of results.

Primary Academic Supervising- Dr Jessie Posar
Email- jposar@uow.edu.au
Project Duration- 8 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Desired background in Physics or Materials Science, with an understanding of organic electronics including their unique material and electrical properties. Experience with Python programming is essential and hands-on experience with optical systems and lasers, as well as electrical measurement techniques, will be highly valued.

The MicroPlus probe, incorporating advanced silicon on insulator (SOI) microdosimeters developed by the Centre for Medical Radiation Physics (CMRP) at the University of Wollongong, was employed to assess the radiation shielding properties of lunar regolith. The aim of these measurements was to determine the optimal regolith thickness required to provide effective protection against harmful space radiation, supporting the design of safer and more sustainable lunar habitats.
The summer project involves analysing the data obtained at GSI Helmholtz Centre for Heavy Ion Research facility, Germany by the CMRP team. Work will involve detector characterisation, calibration in the CMRP lab and data analysis. The student will contribute to paper preparation.

Primary Academic Supervising- Dr Linh Tran
Email- tltran@uow.edu.au
Co-Supervisor- Dr Vladimir Pan
Email- vpan@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Students who have completed Radiation Physics and Nuclear Physics subjects (Phys255/8255).

This project aims to develop a machine learning-based security monitoring system designed to enhance the safety and reliability of smart homes. The system will integrate data from various IoT devices, including cameras, motion sensors, and smart locks, to detect abnormal activities in real time. By applying advanced ML algorithms for behavior analysis and anomaly detection, it can distinguish between normal household patterns and potential security threats. The system will feature adaptive learning to improve accuracy over time and provide timely alerts to homeowners. This project promotes intelligent, automated, and proactive home security management.

Primary Academic Supervising- Dr Chau Nguyen
Email- chaun@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- ML algorithms, Implementation skills, Networking fundamentals, Networking attacks, IoT systems.

Absence of resistance, revolution in energy handling and generation, new superconducting electronics, single photon and single proton detection for space and medicine, quantum supremacy, quantum vortices, high energy particles probing superconducting quantum states, magneto-optical imaging, these are just a few key words highlighting what is explored within this project tailored individually for every interested student with the help of the state of the art equipment and theory.

Primary Academic Supervising- Prof Alexey Pan
Email- pan@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- N/A

Thin film technologies, surface and interfacial sciences, nano-technologies, hybrid structures (magnetism, superconductivity, semiconductors), novel phenomena at interfaces explored by neutrons, high density magnetic storage systems, spintronics (spin-electronics) and novel devices, these are just a few key words highlighting what is explored within this project tailored individually for every interested student with the help of the state of the art ultra high vacuum and low temperature facilities and theory.

Primary Academic Supervising- Prof Alexey Pan
Email- pan@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- 3 year students preferred

Are you interested in applying data science and artificial intelligence to real-world healthcare challenges? This 12-week summer scholarship project provides an opportunity to develop algorithms that automatically identify aged care clinical indicators, including pressure injuries and restrictive practices, from residential aged care records.

Working with anonymised nursing and clinical data, you will apply data mining and natural language processing techniques to uncover patterns that can improve the quality of care and resident safety. By the end of the project, you will have contributed to a prototype system for detecting critical care issues, with the potential for real-world impact in aged care services.

What you’ll gain:

• Hands-on experience in machine learning, data mining, and health informatics.
• Mentorship from academics working at the intersection of AI, healthcare, and ethics.
• A stipend and access to national supercomputing resources.

Primary Academic Supervising- Prof Ping Yu
Email- ping@uow.edu.au
Co-Supervisor- Prof Chao Deng
Email- chao@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project-

• Students in computer science, data science, Artificial Intelligence, or related fields.
• Advanced programming skills (Python) are required.
• A strong interest in healthcare applications of AI.

Are you interested in leveraging cloud computing and cybersecurity to build innovative solutions for healthcare collaboration? This 12-week summer scholarship project offers a unique opportunity to design and develop AWS services for a care collaboration website, ensuring the platform is both functional and secure for real-world healthcare applications. 

You will work on developing and deploying AWS-based solutions tailored to a web platform that facilitates seamless communication and information sharing among healthcare providers, patients, and caregivers. A critical focus of the project will be implementing robust security measures, including data encryption, access control, and compliance with healthcare data protection standards (e.g., HIPAA or GDPR). 

By the end of this project, you will contribute to a prototype care collaboration platform with secure cloud infrastructure, addressing the growing need for reliable, scalable, and secure healthcare web applications. 

 What you’ll gain

• Hands-on experience with AWS cloud services, web development, and cybersecurity. 
• Practical knowledge of implementing secure and scalable cloud solutions for healthcare applications. 
• Mentorship from academics and professionals at the intersection of cloud computing, cybersecurity, and healthcare innovation. 
• A stipend and access to cutting-edge cloud and development tools.   

Primary Academic Supervising- Prof Ping Yu
Email- ping@uow.edu.au
Project Duration- 12 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project-

• Students in computer science, software engineering, cloud computing, or related fields. 
• Experience with AWS services (e.g., EC2, S3, Lambda) and programming skills (Python, JavaScript, or similar) are required. 
• A strong interest in cybersecurity and healthcare applications of cloud technologies. 

A comparative study of dust control technologies to understand the key inputs influencing the removal of dust from an air stream. Project is predominantly experimental utilising an existing test facility to investigate various dust control technologies for the removal of dusts with varying characteristics.

Primary Academic Supervising- Dr Jon Roberts
Email- robertsj@uow.edu.au
Project Duration- 8 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- N/A