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 2026.
Applications open 15 September and close at midnight on 4 October 2026.
Supervisor: Prof Xiaoke Yi, Associate Prof Luping Zhou, Associate Prof Liwei Li
Eligibility: Undergraduate or Master students in Electrical engineering, Computer science, Mechatronics, Computer engineering, Telecommunication, Software engineering
Project Description:
Advanced sensing systems are increasingly important in areas such as healthcare, autonomous systems, environmental monitoring, communications and the Internet of Things. At the same time, modern sensors often generate large, complex and noisy datasets that require intelligent signal processing to extract useful information.
This project will investigate how machine learning and deep learning can be combined with advanced sensing platforms to improve sensitivity, resolution, robustness and measurement accuracy. Depending on their background and interests, students may work on sensor data processing, feature extraction, denoising, classification, parameter estimation, sensor fusion, electrical circuit design, or software implementation.
Students from electrical engineering, computer science, computer engineering, mechatronics, telecommunications, software engineering or related disciplines are encouraged to apply. Experience in Python, machine learning, signal processing, electronics or experimental work would be beneficial, but is not essential.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisors: Prof Xiaoke Yi, Associate Prof Liwei Li
Eligibility: Undergraduate students, or Master students, from electrical engineering, computer engineering, software engineering, data science, mechatronics, telecommunications or related disciplines are encouraged to apply. Experience in Python, machine learning, electronics, simulation or experimental work would be beneficial, but is not essential. No prior background in photonics is required.
Project Description:
Artificial intelligence (AI) is rapidly transforming science, technology, and industry. However, conventional electronic processors are facing increasing challenges in speed and energy efficiency. We are now exploring new computing technologies that use light instead of electricity to perform key AI operations.
This project will investigate emerging light-based computing technologies that enable ultra-fast AI processing with low power consumption.
Students will explore simulation, data processing, or experimental testing of the new photonic AI chips developed at the University of Sydney, which demonstrate the potential of optical hardware for next-generation AI systems.
Undergraduate students, or Master students, from electrical engineering, computer engineering, software engineering, data science, mechatronics, telecommunications or related disciplines are encouraged to apply.
Experience in Python, machine learning, electronics, simulation or experimental work would be beneficial, but is not essential.No prior background in photonics is required.
Requirement to be on campus: No
Supervisors: Prof Xiaoke Yi and Associate Prof Liwei Li
Eligibility:
Undergraduate or Master students. Students from electrical engineering, mechatronics, or telecommunications are encouraged to apply. No prior background in photonics is required.
Project Description:
Future 6G and satellite communication systems will operate at increasingly high frequencies and bandwidths, creating major challenges in signal processing, interference mitigation and system resilience. This project will explore how photonics can be used to process high-speed wireless signals with large bandwidth, low loss and low latency.
Students will investigate photonic approaches for next-generation communication systems, with potential applications including interference cancellation, anti-jamming, spoofing detection and mitigation, and high-speed signal processing. Depending on their background and interests, students may work on system modelling and simulation, signal-processing algorithms, radio-frequency (RF) and photonic experiments, or performance evaluation using realistic communication signals.
Students will gain hands-on experience in RF systems, photonics and communication engineering while working alongside PhD researchers and industry collaborators on real-world wireless communication challenges. Students from electrical engineering, mechatronics, telecommunications or related disciplines are encouraged to apply. Experience in radio-frequency systems, signal processing, communications, MATLAB or Python would be beneficial but is not essential. No prior background in photonics is required.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisor: Prof Gregor Verbic
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:
Renewable-dominated grids must continually balance variable wind and solar generation with electricity demand. Demand flexibility helps by shifting consumption and storage charging to periods of abundant supply, reducing curtailment, peak demand and reliance on costly network or generation capacity. Within homes, behind-the-meter distributed energy
resources—including batteries, electric vehicles, hot-water systems, air conditioning and flexible appliances—can be coordinated by a home energy management system in response to prices, network conditions and household needs.
However, modelling every device individually becomes computationally unwieldy, particularly across thousands of homes. This project asks whether their combined feasible power trajectories can be represented safely by a compact, single-state “virtual battery” with guaranteed disaggregation to individual devices. Applications include faster household scheduling, real-time control, virtual power plants, and scalable retailer, aggregator and network optimisation. The project requires strong linear algebra, convex-set and familiarity with mathematical optimisation. The project is particularly suited tostudents who enjoy mathematical modelling and simulation.
Requirement to be on campus: No
Supervisor: Prof. Yonghui Li, Dr. Haiyao Yu
Eligibility:
Applicants must be familiar with MATLAB. Basic knowledge of probability, signal processing, wireless communications, or numerical optimisation would be helpful but is not essential.
Project Description:
Future 6G networks are expected to sense and localise devices as well as communicate. This project explores how programmable stacked intelligent surfaces, which program many low-cost surface elements, redirect and focus wireless signals to support accurate three-dimensional positioning with low hardware and computational complexity.
Inspired by recent research in wave-domain near-field localisation, the student will develop MATLAB simulations of wireless propagation and intelligent-surface responses, implement representative localisation and search methods, and examine how design choices such as surface size, number of layers, noise level, and measurement overhead affect performance.
The project is intentionally flexible and can be tailored to the student's interests. Possible extensions include improved search strategies, signal-processing techniques, or optimisation methods. Expected outcomes include reproducible MATLAB code and a report presenting performance comparisons against baseline methods, together with practical insights into future low-complexity sensing and integrated sensing-and-communication systems.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisor: Prof. Yonghui Li, Dr. Haiyao Yu
Eligibility:
Applicants are expected to have a foundation in Python, and prior experience with simulation software, such as CARLA or Blender, would be an advantage.
Project Description:
This project focuses on developing dynamic, environment-aware mmWave vehicle-to-everything (V2X) wireless systems. With the rapid advancement of AI-enabled autonomous driving and intelligent transportation systems, future connected vehicles will increasingly rely on high-capacity, low-latency, and highly reliable wireless links to support real-time perception sharing, cooperative decision-making, and V2X communication. Millimeter-wave communication is a key enabling technology for 6G and future mobile networks, offering extremely high data rates but also facing significant challenges caused by blockage, mobility, and rapidly changing propagation environments.
This project aims to investigate how realistic and dynamic urban environments affect mmWave V2X communication performance, and to support the development of more environment-aware and adaptive wireless system designs for future mobility scenarios.
The student will build and configure mmWave V2X simulation environments, collect and process simulation data, and support the reproduction of existing models and experimental testing where required.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisors: Prof. Yonghui Li, Dr. Haiyao Yu, Mr. Yilun Wang
Eligibility:
Project Description:
Radio maps describe how wireless signal strength varies across a physical space, and they underpin coverage planning, positioning, and network optimisation. Such maps are currently produced by ray-tracing simulators, which model every reflection and diffraction path in detail and can take hours per scenario, limiting their use at scale or in real time.
This project aims to replace the simulator with a learned surrogate. It will develop generative deep learning models that project building geometry, material properties, and antenna configuration to the wireless pathloss map within seconds. The generated maps accuracy should have potential to support downstream tasks including localisation, user trajectory prediction, and optimal placement of access points and base stations.
The intern will work alongside PhD students and academic staff on dataset generation, model training, and benchmarking against commercial ray-tracing software. The project sits at the intersection of wireless communications, machine learning, and computer vision.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisor: Dr. Ruigang Wang, Prof. Daniel Quevedo
Eligibility: Control theory; Deep Learning; PyTorch
Project Description:
Training large language models (LLMs) requires enormous computational resources. This project aims to develop more efficient pretraining methods by leveraging ideas from optimal control theory.
We will first interpret the transformer architecture as a nonlinear dynamical system and reformulate pretraining as an open-loop optimal control problem. This perspective enables the use of powerful tools from control theory to analyze and improve the training dynamics of LLMs.
Building on this formulation, we will explore several approaches to improve training efficiency, including controllability and stability analysis of LLMs, moving horizon control strategies, and other control-inspired optimization techniques.
Finally, the proposed methods will be evaluated through large-scale experiments using a state-of-the-art NVIDIA GPU cluster. This project offers an opportunity to work at the intersection of control theory and modern AI, contributing to the development of more efficient and scalable training methods for LLMs.
Requirement to be on campus: Yes (preferred) *as per government’s health advice.
Supervisor: Prof. Yonghui Li, Dr. Geng Wang
Eligibility: Open to students in Electrical Engineering, Computer Engineering, Computer Science, or related disciplines with an interest in wireless communication and artificial intelligence.Strong programming skills and telecommunication engineering background are preferred.
Project Description:
Future wireless systems need to maintain reliable communication and sensing as users move, obstacles appear and signal quality changes. In such dynamic propagation environments, fixed configurations and adaptation rules may not remain effective. Inspired by artificial general intelligence (AGI) concepts of goal-directed decision making, outcome prediction and persistent memory, this project will explore a self-evolving wireless agent for a programmable intelligent metasurface-assisted integrated sensing and communication (ISAC) system. At each step, the agent will observe wireless signal measurements, predict the outcomes of candidate intelligent metasurface configurations, decide the configuration, evaluate and retain the experience to improve future decision strategy. A lightweight closed-loop simulation will evaluate the agent under user mobility, changing blockage conditions, and different signal-to-noise ratios (SNRs), and compare it with fixed and conventional adaptive approaches.
The project aims to provide an accessible proof of concept showing how wireless systems can learn from interaction and progressively improve their operation.
Requirement to be on campus: Yes *as per government’s health advice.
Supervisor: Chentao Yue, Gaoyang Pang, Branka Vucetic
Eligibility: Open to students in Electrical Engineering, Computer Engineering, Computer Science, Mathematics, or related disciplines with an interest in information theory and machine learning. Python programming experience is desirable; familiarity with probability, linear algebra, optimisation, or basic deep learning will be helpful.
Project Description:
Error control coding is a cornerstone of reliable wireless communication, where redundancy is introduced to detect and correct errors caused by noise and channel impairments. An analogous problem arises in AI systems, where model imperfections, uncertainty and distribution shift can lead to erroneous predictions or hallucinated outputs. This project will investigate whether ideas from classical channel coding can be adapted to improve the reliability of AI inference. The student will review relevant literature in coding theory and robust machine learning, formulate coding-inspired mechanisms for representing model outputs, and evaluate their effects on prediction accuracy and robustness across selected AI tasks. The project is expected to contribute to a new research direction at the intersection of communications and artificial intelligence, with potential applications in trustworthy AI, large language models, and safety-critical decision-making systems. Students will gain experience in coding theory, AI modelling and experimental research.
Requirement to be on campus: Yes*as per government’s health advice.
Supervisor: Haonan Zhou, Gaoyang Pang, Yonghui Li
Eligibility: Open to students in Mechatronics, Electrical, Computer or Software Engineering with strong programming ability. Experience in robotics, control, ROS/ROS 2, embedded systems, reinforcement learning, or vision-language-action models is desirable. Students must be comfortable with testing, debugging, and safe laboratory work practices.
Project Description:
Humanoid robots require hierarchical control architectures to translate high-level goals into safe, interpretable, and stable whole-body behaviours. This project will investigate a hierarchical framework for humanoid control, with a simulation-first workflow and, when available, optional supervised hardware validation. The student will assist in implementing interfaces between hierarchical layers, prototyping task decomposition and skill sequencing logic, and evaluating timing budgets, watchdog mechanisms, and recovery behaviour under perception dropouts. Applications include assistive humanoids, collaborative robots, and embodied AI systems operating in semi-structured environments. The internship will provide hands-on experience in robot software architecture, control integration, safety-aware experimentation, and quantitative evaluation, with mentoring for students interested in careers in robotics research.
Requirement to be on campus: Yes *as per government’s health advice.
Supervisors: Dr Litianyi Zhang, Yubo Wen, Prof Yonghui Li
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:
Future wireless devices infer their radio environment fast enough to prevent connectivity loss, guide autonomous machines, and adapt communication resources locally. Existing spatial-sensing receivers depend on antenna arrays, multiple radio-frequency chains, field-programmable gate arrays, and high-rate baseband processing, which raise latency, energy consumption, and hardware cost. This project investigates a passive multiport smart antenna system that encodes an incident wave’s direction and received power into a small set of physically interpretable port signatures. We will combine electromagnetic modelling, prototype measurement, and lightweight AI models to convert these signatures into decisions such as source-sector identification, link-state prediction, and beam or access-point selection. The work will establish a proof-of-concept architecture for low-latency edge inference in integrated sensing and communications, autonomous systems, and resource-constrained Internet-of-Things devices.
Requirement to be on campus: Yes *as per government’s health advice.
Supervisors: A/Prof Steve Shu
Eligibility:
Project Description:
Electron ptychography has emerged as a powerful technique for achieving atomic-scale resolution by capturing coherent diffraction patterns during probe scanning. However, reconstructing high-quality phase images from these patterns is computationally intensive and sensitive to noise, especially when dealing with large-scale datasets from advanced electron microscopes. This project explores the application of deep learning to accelerate and improve phase retrieval in electron ptychography. Traditional iterative solvers, while accurate, are often too slow for real-time imaging. Deep neural networks offer a promising alternative by learning to map diffraction data directly to phase reconstructions, drastically reducing inference time. References: Chang et al., "Deep-learning electron diffractive imaging" (2023); Sadri et al., "Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy" (2024).
Requirement to be on campus: No
Supervisors: A/Prof. Steve Shu
Eligibility:
Project Description:
Modern ptychography enables high-resolution imaging by scanning coherent beams over samples and recording diffraction patterns. However, the limited dynamic range of existing detectors leads to information loss through saturation at high intensities or insufficient signal at low ones. This bottleneck becomes critical in capturing fine structural details, especially under challenging illumination conditions. This project investigates a novel approach to enhance ptychographic imaging by integrating multi-exposure image fusion (MEF) techniques. By collecting diffraction patterns at multiple exposure levels and combining them into high dynamic range (HDR) measurements, the method seeks to preserve rich intensity details across all regions of diffraction space.References: Kodgirwar et al., "Bayesian multi-exposure image fusion for robust high dynamic range ptychography" (2024); Liu et al., "Resolution-enhanced lensless ptychographic microscope based on maximum-likelihood high-dynamic-range image fusion" (2024).
Requirement to be on campus: No
Supervisors: : A/Prof. Steve Shu
Eligibility:
Project Description:
Ptychography is a computational imaging technique that reconstructs high-resolution object images from multiple diffraction measurements. Recently, automatic differentiation (AD) has been introduced into ptychographic reconstruction to enable flexible optimisation and model-based reconstruction. This project aims to investigate and improve the optimisation strategies used in AD-based ptychography algorithms. The student will explore learning-rate scheduling, convergence behaviour, and robustness under noise using simulated datasets. The project will involve implementing and modifying existing reconstruction algorithms, running numerical experiments, and evaluating reconstruction quality using standard metrics. Through this project, the student will gain hands-on experience in computational imaging, optimisation methods, and scientific programming using Python and modern machine-learning frameworks. The outcomes will contribute to improved efficiency and stability of AD-based ptychographic reconstruction methods.
Requirement to be on campus: No
Supervisors: 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.
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: No
Supervisor: Dr Wibowo Hardjawana
Eligibility: WAM ≥ 80. A strong background in wireless communication and a deep learning background equivalent to that covered in ELEC5508 Wireless Engineering 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 natural-language network instructions. 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: Prof. Yang Yang, Dr. Jiexin La
Eligibility: Students should have an interest in electromagnetic technologies and their applications in biomedical sensing. Training in relevant simulation and measurement techniques will be provided as part of the project. Basic knowledge of electromagnetics, antennas, or microwave engineering is desirable. Experience with MATLAB, electromagnetic simulation software, or laboratory measurements would be advantageous.
Project Description:
Early detection of skin cancer is critical for improving treatment outcomes. This project explores non-invasive electromagnetic technologies for detecting changes in the dielectric properties of skin associated with abnormal tissue. Depending on the student's interests and background, the project will focus on one of two research directions:
The first involves the design and optimisation of a dielectric lens to improve electromagnetic focusing and spatial resolution for imaging skin abnormalities.
The second focuses on the development of a metasurface-based sensor with enhanced sensitivity to changes in the dielectric properties of skin associated with abnormal tissue.
Depending on the student’s interests and background, the project will focus on one of these directions. Students will gain experience in electromagnetic simulation, antenna and microwave engineering, design optimisation, and potentially experimental measurement. The project provides an opportunity to apply fundamental electromagnetic concepts to biomedical sensing and contribute to the development of non-invasive technologies for early skin cancer detection.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisor: Prof Glenn Platt
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:
AI noise classification is used to identify different sound sources, such as trains, aircraft, insects and industrial equipment, from recorded audio. This is used in environmental compliance and other industries.
The project will generate audio embeddings, which are compact numerical representations of sound clips. These embeddings can be stored in a vector database, enabling fast searches for recordings that sound similar to a supplied audio sample.
This capability can help automate the labelling of audio data, reduce manual effort during model development, and enable large-scale analysis of noise occurrences across sites and time periods. The outcome will be a searchable audio knowledge base that supports environmental monitoring, acoustic analysis, and the development of future AI-based noise classification tools.
The project would suit a student with interest in data science or artificial intelligence.
Requirement to be on campus: No
Supervisors: Prof Glenn Platt
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 will work with an industry partner to investigate the impact of different training processes on AI-driven noise classification. AI based noise classification is used to determine the source of noise that may be unsafe, impact human comfort, or cause other issues.
The project will experiment with model training processes (specifically training data composition) to explore how models trained on a larger data corpus perform relative to models trained for a specific noise environment.
As an example, can a model trained on data from rural locations (where train noise is typically indicated by large freight trains) perform well when exposed to data from urban environments (where train noise is typically indicated by passenger trains)? – or would 2 separate models with specific training focus deliver better performance?
The project would suit a student with interest in data science or artificial intelligence.
Requirement to be on campus: No
Supervisors: Dr. Cuo (Charlie) Zhang
Eligibility:
Project Description:
his project aims to create a new cooperative architecture for virtual power plants (VPPs). It expects to generate new knowledge in smart grids, developing fundamental techniques to enable VPPs to cooperatively support the operation of power distribution systems, contributing to achievement of Australia’s net-zero emission target by 2050. The anticipated outcomes include new science and knowledge of energy data interoperability among VPPs, new cooperation mechanisms for distributed energy sources (DERs) and VPPs, and an open-source framework for prototype evaluation. This research promises significant benefits, such as enhanced grid sustainability, greater capacity to accommodate DERs, and fortifying the security of the distribution grid.
Phd scholarship for this research topic is available, dependent on the successful completion of the project.
Requirement to be on campus: No
Supervisors: Dr Ian Abraham
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 will develop methods for robot learning and control that operate at the low latencies required for dynamic, contact-rich interaction. The research will integrate real-time optimization, reinforcement learning, and low-level control to enable policies that can be trained efficiently, adapted online, and deployed directly on embedded robotic hardware. A central focus will be reducing the computational and communication delays that arise when learned policies, state estimation, optimization, and feedback control are distributed across conventional software stacks. The project will investigate structure-exploiting optimization, model-based and model-free reinforcement learning, and policy representations designed specifically for high-frequency execution. These methods will be evaluated on tasks requiring rapid force regulation, whole-body coordination, and adaptation to changing physical conditions. The expected outcome is a new class of learning-enabled controllers that combine the adaptability of reinforcement learning with the speed, reliability, and predictability required for deployment at the lowest levels of robotic control.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisor: Dr Wibowo Hardjawana
Eligibility Criteria: WAM >= 80. A strong background in wireless communication and a deep learning background equivalent to that covered in ELEC5508 Wireless Engineering are required. Programming knowledge C/Python are required.
Project Description:
The project aims to develop a basic real-time 6G air‑interface reference design using Software-Defined Radio (SDR). The targeted air‑interface technologies are single-input-single-output (SISO) and orthogonal frequency-division multiplexing (OFDM). The student will implement the signal processing of the targeted technologies in hardware descriptive language (HDL) and import them into an field programmable gate array (FPGA) chip by utilising the following Matlab workflows.
This is a challenging project suited for students interested in advanced wireless systems, FPGA prototyping, and AI‑based signal processing and moving towards in-depth study in wireless communications.
Requirement to be on campus: Yes *dependent on government’s health advice
Supervisors: Dr Ali Shakiba
Eligibility:
Proficiency in Python and comfort with the command line, Git, and running/scripting external tools. Basic understanding of software security concepts (common vulnerability classes, e.g. injection) or willingness to learn. Familiarity with LLM coding agents, prompt engineering, or the skills ecosystem or willingness to learn.
Project Description:
AI coding agents are fast but frequently insecure, studies show a large fraction of generated code carries vulnerabilities, and better models are not fixing this. This project builds a reusable, open-source library of “secure-coding skills” that an agent invokes while generating code: some corrective (running static analysis and revising), some preventive (forcing a threat-model
step, or writing an exploit test before coding). You will build a harness that plugs these skills into an existing agent and run a rigorous comparison, measuring vulnerability rate on a security
benchmark while holding functional correctness constant across multiple models and seeds. The headline question: do preventive skills outperform corrective ones? You will gain agentic-AI and empirical software-security skills.
Requirement to be on campus: No
Supervisor: Dr Shahadat Uddin
Eligibility:
Project Description:
Artificial intelligence has achieved remarkable success in disease prediction, but most current models differ significantly from the way the human brain learns and processes information. This project will investigate how principles from brain organization, such as sparse connectivity, modular structures, adaptation, and efficient learning, can be incorporated into machine learning models to improve disease prediction. Students will explore neuro-inspired AI techniques and evaluate their effectiveness using real-world health datasets. The project offers hands-on experience in machine learning, network science, healthcare analytics, and explainable AI while contributing to the development of more intelligent and trustworthy healthcare systems.
Requirement to be on campus: Yes *dependent on government’s health advice.
Last updated 13 September 2026
