Explore a range of electrical and computer engineering research internships to complete as part of your degree during the semester break.
The following internships listed are due to take place across the summer break 2025-26.
Applications open 10 September and close 30 September 2025.
Supervisor: A/Prof. Weidong Xiao
Eligibility: Microcontroller programming and power electronics
Project Description:
Current–voltage (I–V) characteristics are an important measure of photovoltaic (PV) generators, corresponding to environmental conditions regarding solar irradiance and temperature. The I–V curve tracer is a widely used instrument in power engineering to evaluate system performance and detect fault conditions in PV power systems. The project aims to construct a practical I-V curve tracer showing improved performance of speed, accuracy, and low cost.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisor: A/Prof. Weidong Xiao
Eligibility: Hands-on experience in power electronics and microcontroller.
Project Description:
Vertical axis wind turbines are designed for small-scale power generation and applied for low wind speeds. The output is typically three-phase AC. For home battery systems, the AC to DC conversion efficiency is important to increase the total power of turbines in lower wind speeds. The project aims to develop a highly efficient converter and battery charger for such application. A vertical axis turbine will be provided for experimental test and concept proof.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisor: A/Prof. Dong Yuan
Eligibility: Good programming skill. Familiar with UE5 and C++ are preferrable.
Project Description:
Wireless technology as the backbone of mobile applications has become essential in our daily lives. However, signal fluctuations caused by the unpredictable nature of factors such as moving crowds and improvised events in wireless and mobile applications amplify the complexity of their comprehensive evaluation across diverse real-world environments.
To address these challenges, we developed a digital twin system with learning-based calibration and ray-tracing based real-time wireless propagation simulation capabilities for WMAs. The system can learn the latent state of the environment from real data for high-precision calibration and accurately simulate wireless systems in realistic 3D environment. The candidate will work on the applications of this system, e.g., autonomous driving and smart manufacturing, which will use measurement of wireless signals for training AI models.
Requirement to be on campus: No
Supervisor: A/Prof. Dong Yuan
Eligibility: Familiar with PyTorch and Nvidia CUDA; programming with Python and C++
Project Description:
Recent advances in deep neural networks (DNNs) have substantially improve the accuracy and speed of video analytics. The maturity of cloud computing, equipped with powerful hardware like GPU, becomes a typical choice for such kind of computation intensive DNN tasks. One obstacle, however, is the large amount of data volume of video streams. For example, a self-driving car can generate up to 750 megabytes of sensed data per second, but the average uplink rate of 4G, fastest existing solution, is only 5.85 Mbps.
In order to avoid the effects of network delay and put the computing at the proximity of data sources, edge computing emerges. Nevertheless, edge computer itself is limited by its computing capacity and energy constraints, which cannot fully replace cloud computing. This project will investigate the efficient parallel algorithms for DNN inference tasks on the edge server that equipped with GPUs.
Requirement to be on campus: No
Supervisor: Dr Alex Song
Eligibility: Understanding of basic optics, interest in nanomaterials, and a passion for sustainability
Project Description:
What if your clothes could cool you in summer or keep you warm in winter—without using a single watt of power? In this project, you’ll explore new approaches to thermal regulation by designing nanostructures in fabrics that reflect sunlight while letting body heat escape as infrared radiation. This emerging field has been pioneered in recent years by top institutes such as Stanford, MIT, and USYD, and is now expanding to wearable, climate-adaptive textiles. You’ll contribute to a growing global effort to develop sustainable, energy-free solutions for personal comfort in extreme environments.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisor: Dr Wibowo Hardjawana
Eligibility:
Project Description:
Each generation of cellular communication systems is marked by a defining disruptive air interface technology of its time, such as orthogonal frequency division multiplexing (OFDM) for 4G or Massive multiple-input multiple-output (MIMO) for 5G, leading to advancement in signal processing. Since artificial intelligence (AI) is the defining technology of our time, it is natural to ask what role it could play in 6G signal processing. The project aims to study the benefit of using AI to process 6G air-interface signal. In this case, AI replaces communication processing blocks at the transmitter and receiver. The specific tasks of the project are to modify and/or add existing wireless communication reference design to facilitate AI-based signal processing in the wireless air interface. Students will do mini-research, select reference designs on different 6G air-interface, ranging from MIMO, OFDM or OTFS and implement AI signal processing techniques ranging from generative and discriminative types.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisor: Dr Wibowo Hardjawana
Eligibility: WAM >= 75. A strong background in wireless communication and a deep learning background equivalent to the one covered in ELEC5508 Wireless Engineering is required. Programming knowledge C/Python are required.
Project Description:
5G delivers high data rates, low latency, and massive connectivity. Open RAN (O-RAN) introduces open interfaces and software-driven functions for flexibility, with the Radio Intelligent Controller (RIC) enabling applications to optimise radio resources. This project explores large language models (LLMs) for intelligent resource allocation in O-RAN systems, applying network instructions to be sent as natural language. Students will first deploy and test a 5G system, then develop and evaluate LLM-based RIC applications to improve spectral or energy efficiency. Using tools such as FlexRIC, ns-O-RAN, 5G-LENA, Sionna RT, NS-3, students will gain hands-on experience in 5G deployment, RIC app development, and AI integration.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisor: Dr Wibowo Hardjawana
Eligibility:
Project Description:
5G is the latest mobile network technology, enabling high-speed, low-latency wireless connectivity. Open-source platforms like OpenAirInterface or srsRAN provide a flexible way to prototype and test 5G systems using software-defined radios (SDRs). In this joint project, students will build a small-scale wireless testbed with one base station and one or two user devices, supported by TSS’s Staff Engineers. The task includes configuring the 5G NR protocol stack and FlexRAN radio intelligent controller (RIC) via GUI or Matlab, establishing end-to-end connectivity, and validating data transmission. Students will gain practical experience with SDR hardware, open-source protocol stacks, and real-time wireless system testing.
Requirement to be on campus: Yes, *dependent on government’s health advice.
Supervisor: Dr Sid Assawaworrarit
Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.
Project Description:
This project aims to design and evaluate silicon-based devices that can control the absorption and emission of mid-infrared thermal radiation. Potential applications for this device include energy generation from waste heat, radiative cooling, and sensing.
With the help of computational tools like COMSOL, students will design silicon devices based on common silicon architectures such as MOS and pn junctions and evaluate their thermal emission modulation, fabrication feasibility, and performance trade-offs.
Possible focus areas include (i) fabrication-realisable design, (ii) device optimisation and theoretical limit of performance, and (iii) evaluating device behaviour under varying conditions.
This project suits students with a strong foundation in semiconductor physics, nanotechnology, or materials science
Requirement to be on campus: Yes (preferred) *as per government’s health advice.
Supervisor: Dr Sid Assawaworrarit
Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.
Project Description:
This project explores the design of nanoparticles for cool paint or coatings that can minimise heat absorption from solar radiation while providing an appealing colour appearance and efficiently emitting cooling thermal radiation. This innovation seeks to reduce reliance on air conditioning and promote energy efficiency, particularly beneficial in hot climates.
The project will use computational tools such as Python-based light scattering calculations and COMSOL to model the behaviour of silicon-based nanoparticles. These tools will be used to study: (i) their light absorption characteristics within the solar spectrum, which determine the heat generated when exposed to sunlight; (ii) their light scattering properties, which contribute to colouration; and (iii) how the dimensions of nanoparticles can be adjusted to tune their colour appearance. Both individual particles and ensembles integrated into paint or coatings will be examined to understand how these properties evolve from single particles to a dense collection in the final product.
Requirement to be on campus: Yes (preferred)*as per government’s health advice.
Supervisor: A/Prof. Zihuai Lin
Eligibility: up to 2 students are required for this project. The students participating in this project should have good knowledge on hardware development. Programming skills are essential. The students with average marks above 75 are preferred.
Project Description:
This project will develop novel solutions for IoT based pressure injury monitoring to enable smart hospitals, smart healthcare and provide in-patients with good treatment experience. IoT is a key enabler for our future smart hospital and healthcare to effectively overcome the major problems, such as nursing staff shortness, impractical physical care environments and difficulties in identifying patents’ needs, etc.
In order to enable a fast uptake of the IoT pressure injury monitoring systems, key issues, such as data collection and sensing, pressure injury prediction modelling, and hospital validation, should be addressed. These problems are the major technological obstacles which are preventing industrial partners from further expanding their business in the hospital IoT area. We will develop a smart mat for pressure injury monitoring.
The proposed smart mat consisting of a pressure sensor array and a moisture sensor array will be tested and validated in the Westmead hospital. The collected data will be transmitted to the cloud via WiFi or other wireless networks for data processing and analysis. The developed deep learning algorithms for smart mat will be used to obtain the weight and moisture distribution of the inpatient’s body as well as the respiratory rate.
Requirement to be on campus: Yes *as per government’s health advice.
Supervisors: A/Prof. Zihuai Lin and Prof. Branka Vucetic.
Eligibility: up to 2 students are required for this project. The students participating in this project should have good knowledge on wireless cellular networks, communication theory and signal processing. Matlab, C++ and Python programming skills are essential. The students with average marks above 75 are preferred.
Project Description:
The ambient backscatter communications (AmBC) technology used in IoT systems is a practical application of energy harvesting from ambient radio frequency (RF) signals. In a typical AmBC-empowered IoT system, a passive or semi-passive IoT device harvests RF energy from the ambient RF source (e.g., a cellular network base station (BS) or a Wi-Fi router) to power its circuits, and then the IoT device modulates its information bits onto a sinusoidal signal by intentionally changing its amplitude and/or phase. In this project, we will build an AmBC-empowered battery-free IoT device prototype.
Requirement to be on campus: Yes *as per government’s health advice.
Supervisors: A/Prof. Zihuai Lin, Prof. Branka Vucetic
Eligibility: up to 3 students are required for this project. The students participating in this project should have good knowledge on smart phone APP development. Programming skills are essential.
Project Description:
This project will invent, train, and deploy AI models that produce accurate, comprehensive matching feedback of a patient’s clinical trial outcomes—which we call Patient recruitment system for global medicine companies
Requirement to be on campus: Yes *as per government’s health advice.
Supervisors: Prof. Xiaoke Yi, A/Prof. Luping Zhou, A/Prof. Liwei Li
Eligibility: Year 3/4/5 or Master students Electrical engineering, Mechatronics, Computer engineering, Telecommunication, Software engineering or Computer science
Project Description:
The state-of-the-art sensing technology is rapidly growing and will play a critical role in the near future. For instance, smart phones, which play a significant role in our daily life, have a fingerprint identity sensor that makes it easy for us to access the device, and they also use an ambient brightness sensor to adjust the display brightness, etc.
The project is to deliver the superior, advanced sensing platforms assisted by machine/deep learning to address the important challenges across a diverse range of applications in various fields, particularly in lab-on-chips, Internet of Things, broadband communications and biomedical applications. The internship project focuses on electrical circuits design and data processing as well as machine learning and software programming. The aim is to realize ultra-sensitive, high resolution and extreme-range sensing.
The intern will closely work with a research team including PhD students and postdoctoral research associates. Innovative signal processing and design in both hardware and software will be carried out during the project.
Requirement to be on campus: Yes *as per government’s health advice.
Supervisors: Prof. Xiaoke Yi, Dr Liwei Li
Eligibility: Year 3/4/5 or Master students studying Electrical engineering, Telecommunication, Mechatronics, Computer engineering, Software engineering or Computer science.
Project Description:
Join us in exploring the new applications of radio frequency (RF) photonics in real-world 6G and satellite communication systems, working alongside industrial experts to design, test, and integrate innovative RF front-end systems. We will investigate the potential of RF photonics to overcome traditional system limitations, analyse and optimize system performance, and contribute to novel solutions to solve industrial challenges.
Through this project, you'll gain hands-on skills, enhance your knowledge of photonics, RF systems, and communication engineering, and develop your analytical, problem-solving skills.
Requirement to be on campus: Yes *as per government’s health advice.
Supervisors: Prof Xiaoke Yi, Dr Liwei Li
Eligibility: Year 3/4/5 or Master students studying Electrical engineering, Mechatronics, Computer engineering, Telecommunication, Software engineering or Computer science
Project Description:
Silicon photonics is an emerging area at the intersection of electronics and optics, with applications spanning data centers, high-speed communications, sensors, and the Internet of Things. This internship provides an opportunity to bridge the gap between electronic circuit design and silicon photonics. While silicon photonic circuits operate in the optical domain, they rely heavily on well-designed and tested electronic circuits for driving, control, and readout.
The project will focus on the design and testing of electronic circuits for silicon photonic devices, enabling students with backgrounds in electronics or mechatronics to contribute meaningfully while gaining exposure to integrated photonics. Students will learn to understand the interaction between electronics and silicon photonics, and to measure and analyze circuit-level performance, including signal integrity, noise, and frequency response.
The intern will work closely with a research team of PhD students and postdoctoral research associates. The project is well-suited for students who have an interest in circuits and are motivated to work in a multidisciplinary lab environment.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisor: Dr Thomas Chaffey
Eligibility: Taking or have taken a course in control systems. Familiarity with the Fourier transform. Ability to code in a high-level language such as Julia or Python.
Project Description:
The Scaled Relative Graph is a recently introduced graphical tool, which generalises the Nyquist diagram of a linear, time invariant dynamical system. It allows a control engineer to read off important system properties, such as stability and passivity, directly from the plot, and establish stability of a feedback interconnection from the non-intersection of the SRGs of the individual components.
This project will develop an open source software tool for computing and visualising the Scaled Relative Graph of a system, from its Fourier transform, in three natural geometries: the Argand diagram, the Riemann sphere and the Poincaré disc. Algorithms for computing the Minkowski sums of convex polygons will be adapted to compute the SRGs of parallel and series interconnections of systems, and a method of sampling the SRG of a black-box system will be developed which seeks out worst-case conditions for control design.
Requirement to be on campus: No
Supervisor: Dr Thomas Chaffey
Eligibility: Ability to code in a high-level language such as python or Julia. Familiarity with convex optimisation an advantage.
Project Description:
Maxwell’s principle states that the currents and voltages in a network of resistors minimise the dissipated power, subject to one of Kirchoff’s laws. For networks of elements which are monotone, this allows algorithms from large-scale convex optimisation to be used to simulate the circuit behaviour. However, elements which are both nonlinear and dynamic are not monotone, and such methods cannot be directly applied.
This project will investigate the existence and convergence of optimisation algorithms which apply to such nonlinear and dynamic elements. These algorithms will take the form of splitting algorithms for compositions of monotone operators. The focus will be on testing the convergence of possible algorithms on practical circuit simulation examples, and, if possible, proving the convergence of algorithms which are found to work. A possible extension will be on generalisations of Successive Over-Relaxation to networks of monotone operators.
Requirement to be on campus: No
Supervisor: Dr Thomas Chaffey
Eligibility: Experience with pytorch. Basic circuit theory useful but not essential.
Project Description:
Recent work has shown the equivalence between the class of electrical circuits built using ideal resistors, transformers, gyrators and diodes, and the class of monotone equilibrium neural networks. This project will develop training methods for RTGD networks by bootstrapping existing methods for monotone equilibrium networks, and incorporating circuit parameterisations. The project will explore the expressive class of particular circuit structures, by benchmarking on standard learning problems.
Requirement to be on campus: No
Supervisors: A/Prof. Craig Jin and Dr Kanchana Thilakarathna
Eligibility: We are looking for software/computer engineering students with Python and PyTorch experience and some background in machine learning.
Project Description:
This project will explore “Oracular Scaffolding” — a new framework for trustworthy machine learning that combines neural networks with constructive mathematics to produce predictions that are auditable, explainable, and verifiable. Students will help develop and test small demonstrators in areas such as medical voice biomarkers (health risk prediction) and adaptive learning (student mastery guarantees).
The work blends Python/PyTorch coding with conceptual tools from computer science and applied mathematics, offering an opportunity to contribute to cutting-edge research at the intersection of AI, logic, and engineering. Students will gain experience in software engineering, experimental design, and research communication, while working as part of an active research group. The outcomes may support future publications and applications in both academia and industry.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisors: A/Prof Craig Jin and Dr Cate Madill
Eligibility: We are looking for software/computer engineering students with Python and Matlab programming experience and some background in image processing.
Project Description:
Real-time MRI is a new imaging modality for the human vocal tract. We are the first group in Australia to pursue this imaging procedure and are collecting MRI data at Westmead Hospital. Within this context, we are developing analysis tools for real-time MRI images of the human vocal tract. We are currently exploring linking volumetric and real-time MRI images, numerical acoustic simulations, machine learning algorithms to individualize text-to-speech using MRI images, and phonetic analyses of the data using MATLAB/Python. You will assist us with developing our analytical tools based on your skill set.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisors: Dr Shahadat Uddin
Eligibility: Knowledge with programming and ML
Project Description:
Machine learning (ML) models are increasingly used in decision-making across domains such as healthcare, finance, and criminal justice. However, biased data can lead to unfair outcomes, disproportionately affecting certain groups. This project, Bias Out, Fairness In, aims to conduct a comparative study of data bias removal techniques and evaluate their impact on model fairness. The research will explore preprocessing methods such as reweighting, resampling, and data transformation, and assess their effectiveness using fairness metrics like demographic parity and equal opportunity. Using publicly available datasets, the project will implement and benchmark these techniques across classification tasks. The goal is to identify which methods best mitigate bias while preserving model performance. This study will contribute to the development of fairer ML systems and provide insights for practitioners seeking to improve equity in algorithmic decision-making.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisor: Dr Huaming Chen
Eligibility Criteria: Students should have knowledge about testing, and ideally come from software engineering background.
Project Description:
Multimodal Large Language Models (LLMs), which integrate text, images, and audio, have significantly advanced AI capabilities across diverse applications, including autonomous driving, healthcare diagnostics, and human-computer interaction. However, their increased complexity and multimodal nature introduce critical vulnerabilities, particularly through adversarial prompt attacks. This project will systematically investigate prompt-based security vulnerabilities unique to multimodal LLMs, focusing on identifying, characterizing, and mitigating attacks that exploit input prompts across multiple modalities. Key research activities include developing robust multimodal prompt-attack benchmarks, analyzing vulnerability propagation across different modalities, and creating novel defensive strategies to enhance model robustness. Outcomes will deliver comprehensive insights into the security landscape of multimodal LLMs, establish guidelines for secure prompt engineering practices, and produce effective methodologies to safeguard these models against sophisticated prompt attacks. This research ensures the reliability and secure deployment of multimodal AI systems in safety-critical and high-stakes scenarios.
Requirement to be on campus: No
Last updated 23rd September 2025
