Generative AI has potential for creative tasks. However, due to the nature of text and image generation, it is often challenging and time-consuming to determine if the generated content matches the user’s intention. This project aims to create an AI-driven tool that automatically evaluates content generation based on a given prompt and curated human feedback samples. This evaluation can be used to: 
-Provide feedback to the user to create better prompts.
-Enable reinforcement learning with AI feedback.
This will teach users to write better prompts and save resources in evaluating and fine-tuning models.

Primary Academic Supervising- Dr Rory Sie
Email- rorys@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project-  Python for data exploration and model training; Data processing (e.g. SQL, PySpark); version control and CI/CD pipelines (e.g. GitHub (Actions)); basic front-end development (e.g. Gradio or Streamlit); API development (e.g. ML server, FastAPI); Langchain for reinforcement learning; Container orchestration (e.g. Docker/Podman and Kubernetes)

Deep generative models have improved significantly in recent years to the point where generated fake speech is now indistinguishable from genuine voice. Speaker verification models are developed to identify specific speakers. However, existing speaker verification models can be fooled by fake speech. This project aims to systematically investigate the robustness of speaker verification models against fake speech. The candidate will study various state-of-the-art speaker verification models against fake speech generated by different text-to-speech models. The expected outcomes include discovering insights into factors that affect the robustness of speaker verification models against fake speech.

Primary Academic Supervising- Dr Wei Zong
Email- wzong@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- The candidate should have experience in Python and PyTorch. Experience in evaluating deep learning models is also required.

This project focuses on designing Graph Neural Networks (GNNs) to address node classification problems in the situations where the training data faces the class-imbalanced issue. This project has important applications in social network analysis, fraud detection, recommendation systems, healthcare, and many other areas where class imbalance is prevalent. This project tasks will include data acquisition, algorithm development, system implementation and evaluation. The candidates (majoring in computer science) are expected to have strong experience in machine learning and a strong interest in a postgraduate research study on machine learning and AI.

Primary Academic Supervising- Dr Shixun Huang
Email- shixunh@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project- Fundamental knowledge in machine learning and strong experience in Python programming.

Range queries are a classic problem in spatial databases, designed to return all objects within a specified query region. This problem has also been addressed in trajectory databases, which identify all trajectories that intersect a given region. Trajectories, often sequences of points representing the movement of objects (such as vehicles, pedestrians, or check-in locations in social media), can reveal richer context and complex correlations based on the regions they traverse. In this project, we focus on multiple range queries for trajectories, where the goal is to retrieve trajectories that pass through a specified set of regions. Such queries, particularly over vehicle trajectories, can yield valuable insights into traffic patterns and network dynamics across different urban areas.

Primary Academic Supervising- Dr Hui Luo
Email- huil@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project- The programming skill (e.g., Python) is a must in this project.

Signs of dementia are usually vague making early diagnosis very challenging. While there is emerging 
evidence showing that movement analysis has the potential to contribute to diagnosis/prognosis of dementia, 
knowledge of how movement changes are exactly associated with dementia has been limited. Meanwhile, analyzing electrical activities of the brain during movement by recording electroencephalography (EEG) can give additional clues. However, further efforts are required to pick up useful signals from EEG recording which is usually 
contaminated with artifacts during physical movements. The main aim of this research is to conduct both movement and brain activity analysis together on elderly people to identify clues about what movement and brain signal can tell about cognitive abilities.

Primary Academic Supervising- Dr Winson Lee
Email- ccwlee@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project- Students with good engineering or health care background.

This project explores the implementation of advanced downshifting control strategies in dual clutch transmissions (DCT) for electric vehicles (EVs) to maximise energy recovery during braking. As the automotive industry shifts towards electrification, the efficiency of energy recovery systems becomes critical for extending vehicle range and improving overall performance. This study focuses on the development of an intelligent control algorithm that dynamically adjusts downshifting patterns based on real-time driving conditions and braking intensity. By optimising the engagement of the transmission during regenerative braking phases, the proposed method aims to enhance energy recapture and improve the vehicle's energy management system. Through simulation and experimental validation, the effectiveness of the proposed control strategy will be assessed, highlighting its potential to contribute to more efficient and sustainable electric vehicle operation. The findings of this research could provide valuable insights for automotive engineers and manufacturers seeking to enhance the performance of EVs through advanced transmission technologies.

Primary Academic Supervising- Prof Haiping Du
Email- hdu@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Good knowledge and skills in Matlab programming, dual clutch transmission, motor control, software and harware testing.

The student will design a wet scrubbing system for the control of airborne dust to contribute to a larger project involving a comparative study of current dust control measures for silica and coal dust. The project will require knowledge of mechanical design and fluid mechanics. Once designed, the student will work with the EIS workshop to have the scrubber manufactured. The student will then be expected to commission the system to allow reliable testing to be performed for the broader project.

Primary Academic Supervising- Dr Jon Roberts
Email- robertsj@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Must have completed ENGG252, MECH311 and MECH372.

The student will be required to commission a test facility for the analysis of water sprays for dust control. The project will be primarily experimental requiring the characterisation of dust and sprays, and the performance of various sprays for the capture of airborne dust to be assessed. The student will work in the bulk materials engineering laboratory to perform the work. Some mechanical design and manufacturing tasks can also be completed for the facility if it is of interest to the student. The student should have a good understanding of fluid mechanics to complete the project.

Primary Academic Supervising- Dr Jon Roberts
Email- robertsj@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Must have completed ENGG252 and MECH372.

This project aims to develop a mobile app to determine the swelling behaviour of expansive soils. Expansive soils can cause severe structural damage due to volume changes with moisture variations. The app will utilise geotechnical data, predictive algorithms, and a user-friendly interface to assess soil swelling risk in real-time. The platform will be valuable for engineers, contractors, and developers, enabling informed decision-making and mitigation strategies for construction on expansive soils. This innovation will enhance infrastructure safety and sustainability in geotechnically challenging regions.

Primary Academic Supervising- A/Prof Vinod Jayan Sylaja
Co-Supervisor- Dr Pabasara Wanniarachchige (main contact fro the project)
Email- pabasara@uow.edu.au and vinod@uow.edu.au
Project Duration- 12 weeks
Prerequisites for the Project- Programming knowledge in Python, R or Matlab.  A PhD student has already developed a mathematical equation using geotechnical data for predicting the swelling behaviour of soil. This project requires a student with prior knowledge of programming to covert those equations to a user-friendly application for practising engineers.

Deep reinforcement learning (DRL) has been increasingly applied to develop control software. While superior performance can be achieved with DRL, one critical concern is its applicability to safety-critical domains, such as autonomous vehicles. This project aims to develop monitoring and safeguard methods to ensure safety for DRL models in inference. The student intern’s task is training DRL models by using state-of-the-art algorithms (e.g. DreamerV3) and implementing safeguard methods (advised by the project supervisor) to ensure the models’ safe behaviours. For example, besides achieving high driving performance, a DRL model controlling an autonomous vehicle must avoid any potential accidents. The project deliverables include evaluation experiments for the effectiveness of the safeguard methods.

Primary Academic Supervising- Dr Guoxin Su
Email- guoxin@uow.edu.au
Project Duration- 8 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- The candidate should have learned some AI and deep learning subjects.

This project employs first-principles calculations to investigate the electronic properties of two-dimensional (2D) materials. By using density functional theory (DFT), we aim to understand the electronic band structure, density of states, and charge distribution in 2D materials, such as graphene, transition metal dichalcogenides, or novel heterostructures. The study will explore the effects of different factors, including layer stacking, twist angles, and doping, on the electronic properties. The insights gained will aid in designing 2D materials with tailored properties for applications in electronic, spintronic, and optoelectronic devices, pushing the boundaries of nanoscale technology.

Primary Academic Supervising- Prof Xiaolin Wang
Co-Supervisor- Dr Muhammad Nadeem
Email- xiaolin@uow.edu.au (main contact for the Project) and mnadeem@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)

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
Co-Supervisor- Prof Marco Petasecca
Email- mlerch@uow.edu.au (main contact for the project) and marcop@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the project- Physics, medical physics would be an advantage.

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 Kuo Chin-Hsing
Email- chkuo@uow.edu.au
Project Duration- 8 weeks (at a maximum of 20 hours per week)

Shoalhaven Water and the Australian Power Quality Research Centre (APQRC) at the University of Wollongong are collaborating in the design and trialing of innovative battery energy storage systems aimed at improving the energy resilience of sewage pumping stations. Energy is a significant cost for water utilities, with around 80 per cent of the electricity in the wastewater treatment process used by pumps. One of the research objectives is to develop advanced operation and control strategies for distributed pumping stations using energy storage and variable speed drives. Advanced pumping system control strategies include Model Predictive Control (MPC), which requires the wastewater inflow to the pumping station be forecast over the fixed period which is the control time horizon. The forecasting model needs to account for seasonal and weather dependency on inflows. Forecasting techniques can utilize statistical methods, Markov chains, neural networks that include deep learning networks, and other methods. The student will be required to develop a simulation model for demonstration purposes.

Primary Academic Supervising- Dr Edward Smith
Email- edwards@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)

Pumping consumes around one-fifth of global electricity production, and in the industrial and agricultural sectors this figure is even greater. The existing laboratory demonstration panel for the Shoalhaven Water project uses a fixed load and cannot emulate a real pumping system under dynamic conditions, therefore students must rely on simulation models without any real-world validation. The objective of this project is to provide an engineering design for a pumping system evaluation platform for the building materials laboratory, including design calculations, electrical and hydraulic P&ID diagrams, and list of materials. The testing platform will be utilised for a range of research activities including, a) development and evaluation of pumping system parameter identification and speed control algorithms, b) efficiency testing and energy consumption measurements, c) hydraulic and mechanical design, and d) power quality. The evaluation platform should incorporate supply and destination reservoirs, centrifugal pump, valves and sensors, variable speed drive, and energy storage system. The student should also investigate the use of IoT-enabled smart sensors for the hydraulic and electrical measurements.

Primary Academic Supervising- Dr Edward Smith
Email- edwards@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)

The Australian Power Quality Research Centre in collaboration with Shoalhaven Water has deployed battery energy storage systems (BESS) at six sewage pumping stations (SPS) within the Shoalhaven Heads community, to enhance the energy resilience of the sites during power outages. The field sites have now been operational for 16 months. The project involves the analysis of historic field data to assess the actual performance of the energy storage systems, compared to the theoretical calculations, and to conduct in-situ testing of battery performance through the development of an automated discharge test in collaboration with the Shoalhaven Water controls team. This will enable the determination of the useful capacity and remaining service lifetime of the battery under various pumping scenarios. The data to be analysed includes energy throughput, capacity degradation rates, cell imbalance, system alarms, and faults. The student will be spending time at the Shoalhaven Water operations centre in South Nowra, as well as conducting site visits with field technicians and engineers.

Primary Academic Supervising- Dr Edward Smith
Email- edwards@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)

Load Modelling is off key importance to power system operators. This project will involve both experimental testing of EV loads under various voltage, frequency, and phase disturbances, as well as modelling these behaviours in Simulink. The insights gained will help develop dynamic load models that can better simulate the impact of EV loads on future power systems.

Primary Academic Supervising- Dr Obaidur Rahman
Co-supervisor- Dr Sean Elphick
Email- orahman@uow.edu.au (main contact for the Project) and elpho@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project- An electrical engineering major student would be most suitable for this project.

This project aims to develop a self-locking electrochemical-enabled Liquid Metal Actuator (SELMA). The SELMA is composed of two Galium droplets equipped with copper electrode through wetting mechanism, a temperature control system, and an actuation head. The steady actuation will be achieved by repeatedly reversing the electric field. To lock the position of actuation head, Galium droplet transfers into solid through the temperature control system.

Primary Academic Supervising- Prof Weihua Li
Co-Supervisor- Dr Hongda Lu
Email- weihuali@uow.edu.au (main contact for the Project) and hongdal@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- The candidate is preferred from mechatronic engineering

Geothermal energy extraction from underground mines presents a promising avenue for sustainable energy generation. This research aims to develop a Life Cycle Analysis (LCA) tool using Open LCA to assess the environmental impacts associated with a case study. The anticipated results will be assessing environmental indicators such as greenhouse gas emissions and energy consumption during geothermal heating and cooling using underground mines. The study will provide valuable insights into the environmental sustainability of such projects, considering their life cycle from system implementation to energy generation.

Primary Academic Supervising- Dr Pabasara Wanniarachchige
Email- pabasara@uow.edu.au 
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites for the Project- Knowledge of Life Cycle Analysis and experience in Open LCA

Laboratory testing of brittle materials to investigate fracturing modes and stress tensors for Rockburst.

Primary Academic Supervising- Dr Justine Calleja
Email- jcalleja@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project- Completed CMEA323 or MINE923

Antibiotic resistance genes (ARGs) in treated wastewater effluent is a significant concern as it can spread residual ARGs from waste to the environment. Sand filters are used in many wastewater treatment plants after the secondary treatment to enhance the effluent quality. They can serve as an important barrier before the release of ARGs in wastewater effluent. This project will investigate the performance of sand filters in removing different ARGs by studying three local Wastewater Treatment Plants. Wastewater samples and filter media samples will be obtained to understand the removal mechanisms and impacting factors.

Primary Academic Supervising- A/Prof Guangming Jiang
Email- gjiang@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites for the Project- Some basic understanding of wastewater treatment and data analysis (bioinformatics).

This project aims to develop deep learning and GPS mapping tools for satellite imaging applications. These tools are intended for large-scale real-time monitoring of oceanic events, maritime vessels, and other targets. The project tasks will include image acquisition, data annotation, algorithm development, system implementation, deployment and evaluation. This project seeks 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 and a strong interest in a postgraduate research study on machine learning and artificial intelligence.

Primary Academic Supervising- Prof Son Lam Phung
Co-supervisor- Dr Hoang Thanh Le
Email- phung@uow.edu.au (main contact for the Project) and tlhoang@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)

The project aims to review the design of the existing 3U CubeSat in SECTE and design a new flight ready CubeSat.

Primary Academic Supervising- A/Prof Raad Raad
Co-supervisor- Dr Faisel Tubbal
Email- raad@uow.edu.au (main contact for the Project) and faisel@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)

Are you passionate about applying AI and machine learning techniques to make a real-world impact? The overall goal of this research is to develop a portable device for assistive navigation of blind people. This project aims to develop deep learning tools for scene perception and multilingual speech interfaces, two important components of the assistive device. The project tasks will include image 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 major. 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 research in AI and machine learning.

Primary Academic Supervising- Prof Son Lam Phung
Co-supervisor- Dr Hoang Thanh Le
Email- phung@uow.edu.au (main contact for the Project) and tlhoang@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)

This project will use photogrammetry and Lidar techniques to capture realistic and geometrically accurate 3D models of buildings and artefacts.

Primary Academic Supervising- Prof Timothy McCarthy
Co-supervisor- Dr Emily Yap
Email- timmc@uow.edu.au (main contact for the Project) and eyap@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites- Willingness to learn programming in RealityCapture, Unity Gaming Engine or similar

This project will develop a home energy and indoor environment quality monitoring kit using zigbee enalbed devices such as Aqara.  The aim is to create a mobile energy audit smart home kit that can be remotely accessed.  This will form the basis of a mobile laboratory for use in UG teaching.

Primary Academic Supervising- Prof Timothy McCarthy
Co-supervisor- Dr Emily Yap
Email- timmc@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites- Desire to learn zigbee or other open source coding for home automation.

Proton therapy holds the promise of revolutionizing the treatment of cancer where tumours grow in critical areas. It is possible to optimise the clinical treatment field using treatment planning system to avoid critical organs to be hit by high linear energy transfer (LET) protons. 
LET optimised plans for brain cancer treatment has been introduced at Groningen Proton therapy centre, Netherlands and the µ+ probe was recently 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 learn Geant4 simulation to compare simulation results with experimental results.

Primary Academic Supervising- Dr Linh Tran
Co-supervisor- Prof Anatoly Rozenfeld
Email- tltran@uow.edu.au (main contact for the Project) and anatoly@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites- Matlab, junior C++ skills, Excel, Medical Radiation Physics background/degree

Extended reality (AR/VR/XR) is increasingly used for workforce training, education, research, and community outreach and consultation programs. Immersing users in realistic simulations of real-world environments has demonstrated enhanced engagement and better translation of learning and training outcomes to real-life scenarios. However, the workflow (3D scanning, modelling, rendering) to digitally construct environments such as buildings and cultural heritage sites remains resource intensive and computationally demanding. This project involves the development of a framework to streamline the process of digitising real environments into spatial visualisation formats for XR applications.

Primary Academic Supervising- Dr Emily Yap
Co-supervisor- Prof Timothy McCarthy
Email- eyap@uow.edu.au (main contact for the Project) and timmc@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites- Must have: software programming skills, Optional and willing to learn: 3D modelling and game engine development

This project investigates the mechanical behavior of concrete-filled steel tubular (CFST) columns, focusing on their structural performance and potential applications in sustainable construction. A numerical study using ABAQUS will assess critical factors, including bond performance, tube geometry, surface roughness, diameter-to-thickness ratios, and slenderness ratios etc. The research will also evaluate various sustainable concrete infill materials, emphasizing their environmental benefits and mechanical properties. Additionally, the research will explore enhancements provided by various internal stiffeners. These findings will serve as a pilot study, offering valuable insights for future experimental testing and advancing wire-arc additive manufacturing (WAAM) technology in steel-concrete composite structures.

Primary Academic Supervising- Dr Mengzhu Diao
Email- mdiao@uow.edu.au
Project Duration- 12 weeks (at a maximum of 30 hours per week)
Prerequisites- Students will have studied CIVL311 and CIVL314.

Ground movement, resulting from events such as landslides, earthquakes, and subsidence, poses a significant threat to the integrity of natural gas pipelines. Understanding the potential impacts of ground movement is crucial for pipeline operators to implement effective mitigation strategies and ensure the safety and reliability of their infrastructure. This project aims to: 1) conduct a thorough literature review of related research, and 2) develop a finite element model to simulate the complex interaction between ground movement and pipeline deformation. The developed model will be used to investigate the effects of different ground movement parameters on pipeline deformation and identify critical failure mechanisms.

Primary Academic Supervising- Professor Cheng Lu
Email- chenglu@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
 

The curve shortening flow (CSF) is a geometric evolution equation where a smooth curve in the plane evolves over time such that each point on the curve moves in the normal direction with speed equal to its curvature. This flow is a fundamental object of study in differential geometry and has significant applications in physics, materials science, and image processing.
 In the classical setting, much attention has been given to the evolution of closed convex curves under CSF, culminating in the celebrated result that such curves shrink to a round point in finite time while becoming asymptotically circular [Gage & Hamilton, 1986]. However, the behaviour of curves with fixed endpoints under CSF introduces additional complexity due to boundary conditions, leading to rich and less explored mathematical phenomena. 
This project aims to delve into the study of curve shortening flows with fixed endpoints. By understanding how these curves evolve, we can gain insights into more general geometric flows and their applications.

Primary Academic Supervising- Dr Glen Wheeler
Co-supervisor- Dr Valentina Wheeler
Email- glenw@uow.edu.au (main contact for the Project) and vwheeler@uow.edu.au
Project Duration- 8 weeks (at a maximum of 20 hours per week)
Prerequisites- Differential equations and elementary differential geometry

This project focuses on the design and implementation of a miniaturized circularly polarized (CP) antenna for CubeSat communications. The antenna is optimized for compact size to fit within the space constraints of 1U CubeSats (i.e., 10cm×10cm×10cm), while maintaining good radiation performance for reliable communication links. Achieving CP reduces signal degradation due to misalignment and multipath interference in Low Earth Orbit (LEO) environments, during CubeSat maneuvers in space. This design aims to achieve high gain, wide axial ratio bandwidth, and wide impedance bandwidth, making it suitable for various CubeSat missions. Simulation (using HFSS or CST) and experimental validation (using a VNA and an anechoic chamber) will be conducted to ensure the antenna meets mission requirements.

Primary Academic Supervising- Dr Faisel Tubbal
Co-supervisor- Associate Professor Raad Raad
Email- faisel@uow.edu.au (main contact for the Project) and raad@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)

This project aims to implement a practical AI-driven model for detecting emergency situations and generating health alerts within a home monitoring system. The system will automatically generate and send alerts to health professionals if it detects any unusual behaviour within a given environment, enabling rapid response to emergency situations. Such a system will have widespread benefits, for example, monitoring elderly individuals in their own home for potential emergencies.

Primary Academic Supervising- Dr Chau Nguyen
Email- chaun@uow..edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
Prerequisites- should ideally have a general background in practical implementation, data analysis and processing, and artificial intelligence.

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 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. Students should have strong aptitude for experimental work, signal processing, 3D printing, good programming experience as well as knowledge of physiology.

Primary Academic Supervising- Dr Manish Sreenivasa
Email - manishs@uow.edu.au
Project Duration - 12 weeks (at a maximum of 30 hours per week)
Prerequisites - should have strong aptitude for experimental work, signal processing, 3D printing, good programming experience as well as knowledge of physiology

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- Professor Alexey Pan
Email- pan@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)
 

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- Professor Alexey Pan
Email- pan@uow.edu.au
Project Duration- 10 weeks (at a maximum of 20 hours per week)