Taste of Research Summer Scholarships

2027 Projects - School of Electrical Engineering and Telecommunications

Electrical Engineering & Telecommunications Research Areas

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Projects offered by other Engineering Schools that may be of interest.

Electrical Engineering & Telecommunications Projects

No School Research Area

Project Title:	3D printing of silica optical fibre preform
Name of Supervisor:	Gang-Ding Peng
Email of Supervisor:	G.Peng@unsw.edu.au
Name of Joint/Co-Supervisor:	Qingqin Han, Guanghao Li
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Advanced Manufacturing and Processing Technologies
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Our modern society is underpinned upon advanced photonic networks that connect people, environment, data and things and expand from telecommunication into sensing, testing, monitoring and control in the form of the Internet of Everything. This expansion creates great need for new silica optical fibres of sophisticated structure designs and mixed material compositions for a great variety of functionalities including sensing, testing, computing, processing, storing, retrieving, as well as transmitting, massive data and information. 3D printing of silica preforms has great potential in fabricating special optical fibres by realising advanced fibre designs and material compositions and allowing fast prototyping and low cost production, with enhanced functionalities, essential for many new and important applications in areas such as medicine, industry and defence. This project will investigate key material and process problems influencing the fabrication process and optical performance of these preforms. In particular, we will explore various factors, such as feedstock composition and process control parameters, in 3D printing and processing silica preforms by UV, thermal or extrusion techniques. In this project, together with PhD students engaging similar research projects, the ToR student will study, design, fabricate and test fibre preforms using UV, thermal and / or extrusion related 3D printing techniques.
Research Environment:	The work will be conducted in Photonics and Optical Communications Group (POCG) in School of Electrical Engineering and Telecommunications at UNSW. The POCG has the state-of-the-art photonic fibre fabrication and testing facilities and has been active at the forefront of photonics and optical fibres research and development for many years. As a member of POCG, the ToR student will have the opportunity to expose to real-world technical and research problems in an environment facilitating teamwork and active interaction.
Novelty and Contribution:	.
Expected Outcomes:	Sound understanding and practical demonstration in 3D printing of silica fibre preforms One technical report highlighting the main findings from the project work One presentation within the POCG reporting on progresses and problems in the project work
Reference Material Links:	For full list of reference material, please refer to: https://www.unsw.edu.au/engineering/student-life/undergraduate-research-opportunities/advertised-taste-research-areas
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	AI-based Network Behaviour Characterisation for Secure Management of Connected Systems
Name of Supervisor:	A/Prof Hassan Habibi Gharakheili
Email of Supervisor:	h.habibi@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Modern buildings, transport systems and enterprises increasingly depend on Internet?of?Things (IoT) and Operational Technology (OT) devices such as cameras, sensors, lighting systems and alarms. These devices are produced by diverse manufacturers and deployed at scale, making them difficult to monitor and secure. As a result, cyber?attacks targeting IoT/OT environments can disrupt essential services and critical infrastructure. Existing monitoring approaches remain limited, with few reliable methods for automatically understanding device behaviour or detecting abnormal and potentially malicious activity in real time. This project aims to develop advanced artificial intelligence techniques to characterise and analyse the network behaviour of IoT/OT devices. By improving the robustness and scalability of machine learning models for traffic analysis, the project seeks to enhance cybersecurity management in large?scale cyber?physical environments. You will join a research team within the School of Electrical Engineering and Telecommunications, working closely with academic supervisors, researchers and honours students. The project also includes collaboration with industry partners interested in evaluating the developed methods in real operational networks, providing hands?on experience with commercial IoT devices and real?world traffic data. Situated at the intersection of networking and applied machine learning, the project aims to deliver scalable, low?cost analytical tools that strengthen cybersecurity resilience. Eligibility is limited to domestic students due to potential access to sensitive environments.
Research Environment:	This project will be conducted within a vibrant and collaborative research group comprising PhD students, honours students, postdoctoral researchers, and academic staff at UNSW. The team also works closely with industry partners interested in trialling and evaluating research outcomes in real operational networks. You will have access to commercial IoT devices and real network traffic datasets (both live and pre-recorded packet traces). The research environment emphasises theoretical development and practical validation, enabling your software tools and algorithms to be tested in realistic network conditions. Regular group meetings, research discussions, and mentorship from senior researchers will support your learning and research development.
Novelty and Contribution:	.
Expected Outcomes:	Expected outcomes of this project include: 1. Development of: (a) novel algorithms for characterising the network behaviour of individual IoT device types, (b) methods for identifying relationships and dependencies between different IoT devices, (c) techniques for improving the interpretability and controllability of machine learning-based traffic classifiers. 2. Prototype software tools for analysing and classifying IoT network traffic. 3. A written scientific technical report documenting research findings. High-quality results may contribute to publications in international conferences or journals, and selected prototypes may be trialled in live operational networks through industry collaboration.
Reference Material Links:	The UNSW research team has published many research articles in this area, which can be found at A/Prof. Gharakheili’s website: https://www2.ee.unsw.edu.au/~hhabibi/publications.html. A recommended starting point is our recent paper: A. Sivanathan et al, “Real-Time and Trustworthy Classification of IoT Traffic Using Lightweight Deep Learning”, IEEE Transactions on Network Science and Engineering (https://www2.ee.unsw.edu.au/~hhabibi/pubs/jrnl/25tnse.pdf).
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Building a Brain in Silicon: Artificial Neurons, Synapses and Learning
Name of Supervisor:	Dr Shimul Kanti Nath
Email of Supervisor:	shimul_kanti.nath@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr Deepak Mishra
Email of Joint/Co-Supervisor:	d.mishra@unsw.edu.au
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Artificial intelligence is transforming intelligent sensing, robotics and autonomous systems, but today’s AI hardware can consume substantial energy because memory and processing are physically separated. Neuromorphic computing takes inspiration from the brain to develop energy-efficient hardware for edge AI, intelligent sensing and brain-inspired computing. In this project, the student will use Python and circuit simulation to build and investigate artificial neurons that integrate, fire, spike and oscillate, and artificial synapses that remember and learn through tunable synaptic weights. The models will be based on emerging memristive and nonlinear electronic devices, providing a direct connection between device physics and next-generation AI hardware. The student will progressively combine neuron and synapse models to investigate simple neuromorphic circuits and networks, including signal propagation, temporal response and learning behaviour. Where appropriate, experimentally measured characteristics of emerging electronic devices will be incorporated to explore how realistic hardware behaviour influences computation. No prior neuroscience background is required. The project is suitable for students interested in electronics, artificial intelligence, programming, circuit simulation or brain-inspired computing, and provides an interdisciplinary introduction to how emerging electronic devices can be translated into functional intelligent hardware.
Research Environment:	The student will join the School of Electrical Engineering and Telecommunications at UNSW Sydney under the supervision of Dr Shimul Kanti Nath and Dr Deepak Mishra. Dr Nath specialises in emerging electronic materials, memristors, nonlinear oscillators, optoelectronic devices and neuromorphic hardware. The student will engage with ongoing work on phase?transition and memristive devices, using measured characteristics to build realistic computational models of artificial neurons and synapses. Dr Mishra contributes expertise in signal processing, machine learning and intelligent sensing, supporting progression from device?level models to network?scale neuromorphic computation. The project offers interdisciplinary training in electronic devices, circuit modelling, AI and scientific programming.
Novelty and Contribution:	.
Expected Outcomes:	The project involves modelling and simulating artificial neurons, synapses and simple neuromorphic networks. The student will develop neuron models that reproduce integration, threshold firing and spiking behaviour, and create synapse models incorporating tunable weights, potentiation, depression and basic learning rules. These models will be integrated into small neuromorphic circuits to study signal transmission, temporal dynamics and network behaviour. Where relevant, experimentally measured memristive or nonlinear device characteristics will be incorporated to enable hardware?aware modelling. The project also includes exploring applications in energy?efficient edge AI and intelligent sensing, with outcomes suitable for publication and poster presentation.
Reference Material Links:	1. W. Zhang, B. Gao, J. Tang, P. Yao, S. Yu, M. Chang, H.-J. Yoo, H. Qian and H. Wu, ‘Neuro-inspired Computing Chips’, Nature Electronics, 3, 371–382 (2020). 2. S. K. Nath, ‘A Light-Driven Device for Neuromorphic Computing’, Light: Science & Applications, 14, 37 (2025). 3. S. K. Nath, S. K. Das, S. K. Nandi, C. Xi, C. Marquez, A. Rúa, M. Uenuma, Z. Wang, S. Zhang, R. Zhu, J. Eshraghian, X. Sun, T. Lu, Y. Bian, N. Syed, W. Pan, H. Wang, W. Lei, L. Fu, L. Faraone, Y. Liu and R. G. Elliman, ‘Optically Tunable Electrical Oscillations in Oxide-Based Memristors for Neuromorphic Computing’, Advanced Materials, 2400904 (2024).
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Constructing Radio Maps for 5G Communications
Name of Supervisor:	Wei Zhang
Email of Supervisor:	w.zhang@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Embedded Systems and Communications
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	This project focuses on constructing radio maps for 5G communications. Radio maps are spatial databases that characterize signal power, channel gain, and interference levels across a geographic area. The key research is reconstructing accurate radio maps from sparse and irregular measurements. This research will explore data-driven approaches, including machine learning techniques, to learn spatial correlations and reconstruct radio maps from limited samples. The expected contributions are novel algorithms for radio map construction validated using 5G measurements, providing practical frameworks for enhancing network intelligence and performance.
Research Environment:	This research will leverage computational resources with machine learning frameworks, alongside access to some 5G measurement datasets and collaboration with Postdoc for writing research papers.
Novelty and Contribution:	.
Expected Outcomes:	Expected outcomes include new machine learning-based methods for radio map construction, experimental validation, and contributions to more efficient network planning and interference management.
Reference Material Links:	https://ieeexplore.ieee.org/document/11008499 https://ieeexplore.ieee.org/document/11397522
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Machine Learning-aided Integrated Sensing and Communications
Name of Supervisor:	Dr Akram Shafie
Email of Supervisor:	akram.shafie@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr. Zhitong Nie, Prof. Jinhong Yuan
Email of Joint/Co-Supervisor:	j.yuan@unsw.edu.au
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Embedded Systems and Communications
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	A new wave of transformative wireless applications—such as augmented and virtual reality, smart factories, V2X connectivity, high?speed rail and low?altitude platforms—is placing unprecedented demands on future networks. These systems require not only high?capacity communication but also accurate radar?like sensing. Integrated sensing and communications (ISAC), a key enabler for 6G, addresses this need by combining sensing and communication within a unified framework that shares hardware, spectrum, waveforms and signal processing. A central challenge in ISAC is target sensing, where parameters such as range, velocity and angle?of?arrival must be estimated in dynamic environments characterised by severe Doppler dispersion and off?grid components. Traditional methods—including MUSIC, compressed sensing and ESPRIT—are often too computationally intensive for resource?constrained receivers. This project develops an intelligent, machine?learning?based sensing framework tailored for dynamic ISAC scenarios. The approach shifts high?dimensional parameter estimation to an offline training stage, enabling low?latency inference during real?time operation. The potential of generative adversarial networks will be explored, supported by novel preprocessing techniques to improve training stability. Models will be optimised to achieve sensing accuracy approaching the Cramér–Rao bound. An adaptive transfer?learning strategy will also be designed to initialise GANs with pretrained weights, allowing rapid online adaptation to evolving channel conditions.
Research Environment:	The student will be hosted at the Wireless Communications Lab (WCL) within the School of Electrical Engineering and Telecommunications at UNSW. The lab fosters a dynamic research environment, with several PhD students, senior research associates, and academics actively working on the related topics in wireless communications.
Novelty and Contribution:	.
Expected Outcomes:	1) Research Contributions: Development of a machine learning model for accurate sensing in dynamic channels, accompanied by a poster presentation and a brief video summarising key findings. 2) Publications: Submission of a short conference paper, to be submitted within two months after project completion. 3) Student Training & Development: The student will gain hands-on experience with MATLAB, Python, and statistical signal processing, along with deep exposure to wireless communication technologies, fostering their interest in pursuing higher-degree research (HDR/PhD).
Reference Material Links:	[1] K. Huang, A. Shafie, M. Qiu, E. Aboutanios and J. Yuan, "A Novel ISAC Waveform Based on Orthogonal Delay-Doppler Division Multiplexing With FMCW," in IEEE Transactions on Wireless Communications, 2026 [2] Q. Cheng, Z. Shi, J. Yuan and H. Lin, "MIMO-ODDM Signal Detection: A Spatial-Based Generative Adversarial Network Approach," in IEEE Transactions on Wireless Communications, Sept. 2024
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Quantum heat simulations for silicon quantum computers
Name of Supervisor:	Nard Dumoulin Stuyck
Email of Supervisor:	n.dumoulin@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Understanding how heat and vibrations propagate at the nanoscale is critical for modern technologies such as nanoelectronics, quantum devices, and thermoelectric materials. At these length scales, lattice vibrations, known as phonons, play a dominant role in determining thermal and mechanical properties. In this project, the student will explore phonon dispersion relations in crystalline materials using computational materials modelling. The work will begin with a structured literature review to build foundational knowledge in solid state physics, lattice dynamics, and nanoscale effects. The student will then learn to perform and analyse phonon dispersion simulations using established computational tools. By the end of the project, the student will be able to interpret phonon band structures and relate them to physical properties such as thermal conductivity and stability. This project provides a hands on introduction to computational research in nanoscale materials science and is well suited for students considering honours or postgraduate research.
Research Environment:	The research will be conducted in the SiMOS Quantum Dot lab, led by Prof. Andrew Dzurak. The research group comprises a dynamic team of academics, research staff, and students, providing a collaborative and supportive environment for cutting-edge research.
Novelty and Contribution:	.
Expected Outcomes:	• Understand basic lattice dynamics and phonon theory • Read and critically assess research literature • Perform phonon dispersion simulations using computational tools • Interpret phonon band structures and physical implications • Communicate scientific results clearly in written and oral form
Reference Material Links:	Vandersypen, L.M.K., Bluhm, H., Clarke, J.S. et al. Interfacing spin qubits in quantum dots and donors—hot, dense, and coherent. npj Quantum Inf 3, 34 (2017). https://doi.org/10.1038/s41534-017-0038-y
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Rethinking AI: Moving from Deep Learning to Assembly Calculus
Name of Supervisor:	A/Prof. Vidhyasaharan Sethu
Email of Supervisor:	v.sethu@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	The aim of a Taste of Research project in this area is the preliminary investigation and development of a novel idea that explores the use of Assembly Calculus models in speech processing.
Research Environment:	The work environment will be within the UNSW Speech Processing Lab in the School of Electrical Engineering and Telecommunications. The group is home to 4 full-time academic staff members, multiple research students, and postdoctoral research fellows, all of whom are actively engaged in research related to speech processing, emotion recognition, disordered speech, and mental state detection.
Novelty and Contribution:	.
Expected Outcomes:	At the completion of the project, the student will have a good understanding of assembly calculus models, AI, speech signal processing and statistical modelling more broadly. In addition, they will have significantly strengthened their technical/research skill as well as programming skills in MATLAB, Python, or Julia. It is anticipated that by the end of the project, the student will have developed and validated an assembly calculus based model for a chosen speech processing system and implemented this as easy to use code/toolbox in MATLAB, Python, or Julia with documentation. If the topic were extended into an honours thesis, more would be possible.
Reference Material Links:	Contact v.sethu@unsw.edu.au or drop by EE442 to discuss the topic. (Enquiries are encouraged). https://www.pnas.org/doi/abs/10.1073/pnas.2001893117 https://arxiv.org/abs/2406.07715 https://arxiv.org/abs/2603.16923 https://direct.mit.edu/neco/article/37/1/193/124822/Computation-With-Sequences-of-Assemblies-in-a
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Simulating Spin Qubit Control in Silicon Quantum Dots
Name of Supervisor:	Associate Professor Henry Yang
Email of Supervisor:	henry.yang@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr Gerardo Paz Silva
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Spin qubits in silicon quantum dots offer a promising pathway for quantum computing due to their relatively long coherence times. However, operational efficiency is challenged by decoherence mechanisms, such as nuclear spin hyperfine interactions and spin-orbit coupling effects. This project employs advanced simulation tools to analyse the noise contributions affecting qubit performance. The objective is to develop and optimize control sequences to mitigate these noise sources, enhancing the qubits' overall performance and stability for quantum computing applications. This project is suitable for exceptional students with backgrounds in electrical engineering, quantum engineering, physics, computing, mathematics, or related fields. A solid understanding of quantum physics, mathematics, and computing is essential.
Research Environment:	The research will be conducted in the SiMOS Quantum Dot lab, formerly led by Prof. Andrew Dzurak. The research group comprises a dynamic team of academics, research staff, and students, providing a collaborative and supportive environment for cutting-edge research.
Novelty and Contribution:	.
Expected Outcomes:	Utilise simulation techniques to explore the impact of noise on the dynamics of spin states, critical for quantum information processing. Apply wavefunction evolution or density matrix methods to understand noise influences. Inform the research group on how different noise types affect various operating protocols. Develop optimised control strategies to significantly improve the fidelity of quantum operations.
Reference Material Links:	Guido Burkard, Thaddeus D. Ladd, Andrew Pan, John M. Nichol, and Jason R. Petta, Semiconductor spin qubits, Rev. Mod. Phys. 95, 025003 (2023) https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.95.025003
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Simulation-Based Defect Detection in Terahertz Microresonators
Name of Supervisor:	Dr Dominik Vogt
Email of Supervisor:	d.vogt@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Electrical Engineering and Telecommunications
For CSE and EET Projects:	School Project
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Terahertz (THz) microresonators enable extreme field confinement and high Q resonances, key ingredients for next-generation communication (6G), spectroscopy, and on-chip THz photonics. Their performance hinges on exceptionally high-quality factors (Q) and pristine mode structures. Any defects will deteriorate their performance. This project investigates the use of electromagnetic simulations to detect and characterise defects in terahertz (THz) microresonators. A reference resonator will be modelled and compared with structures containing controlled geometric or material defects. Changes in resonant frequency, Q-factor, spectral response, and electromagnetic field distribution will be analysed to assess the resonator's sensitivity to different defects. The project will develop a simulation framework that can support non-destructive defect detection and quality control of THz devices, with potential extension to automated or machine-learning-based defect identification.
Research Environment:	The project will be conducted in the Terahertz Innovation Lab at UNSW, providing access to a collaborative research environment with regular weekly group meetings and support from peers for discussion, problem-solving, and knowledge exchange.
Novelty and Contribution:	.
Expected Outcomes:	The project is expected to provide insight into how different types of defects affect the resonant behaviour of THz microresonators, including how the response varies with defect type, size, and location.
Reference Material Links:	https://doi.org/10.1063/1.5010364
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Projects offered by other Engineering Schools that may be of interest are:

Graduate School of Biomedical Engineering

School of Computer Science and Engineering