Industry Research Program Projects
Expressions of interest are now open for a range of exciting industry led research projects spanning multiple discipline areas including Data Science, Electrical Engineering, Electronic Engineering, Mechanical Engineering, Computer Science, IT, Software Engineering, Telecommunications, Mathematics, Machine Learning, Physics, Telecommunications, Robotics and Human Factors.
Please note: submitting an Expression of Interest does not guarantee a scholarship, but it is a necessary first step in the application process.
Discover available projects
Project Summary: CAE and UNSW will examine situational awareness and cognitive load for autonomous systems. Specifically, it is aimed to understand how situational awareness is affected by different levels of cognitive load, with the focus on high demand tasks, such as the operation of sensors in unmanned aerial vehicles (UAV’s). The proposed study will employ valid and objective data from simulation and biometrics such as gaze tracking, heart rate variability and galvanic skin response to develop new interpretative algorithms to inform training task instructors on the trainee cognitive load in performing operational tasks.
The research will further develop and investigate the efficacy of the cognitive load assessment algorithm followed by the developed similar algorithms for flight based tasks by CAE. All scientific support, including knowledge and resources will be available to the candidate.
The research tool will be a CAE UAV flight and sensor simulator, equipped with biometric sensors, with a suite of training scenarios available for task evaluation. The simulator will be fully supported, hosted at UNSW and available for the duration of the program. The biometric device will be provided by UNSW. The project will involve data collection from human subjects and will be required to receive ethics application approval prior to the start of the experiment. This support will be provided by the adviser om UNSW.
Industry Partner: CAE Australia
University: UNSW Canberra
Academic Supervisor: Dr. Oleksandra Molloy
Email: o.molloy@unsw.edu.au
Offered for: Doctor of Philosophy (PhD), Master of Philosophy (MPhil)
Relevant discipline areas: Learning Science, Biometrics, Psychology, Human Factors, AI, Adaptive Learning
Project Summary: The increasing deployment of Machine Learning (ML) models in critical applications has led to concerns about their vulnerability to adversarial attacks. Adversaries can exploit vulnerabilities in these models to manipulate their outputs or performance. This research project aims to investigate the development of surrogate ML models through exploratory attacks, design novel adversarial attacks on these surrogates, and investigate the transferability of these attacks to the original models.
The project addresses the growing concerns about adversarial attacks on ML models by building surrogate models through exploratory attacks, designing targeted adversarial attacks, and evaluating their transferability and impact on the original models. The anticipated outcomes of this study will contribute to enhancing the understanding of model vulnerabilities and exploring potential defence mechanisms against adversarial attacks in real-world applications.
Industry Partner: DEWC Services
University: University of Adelaide
Academic Supervisor: Professor Frank Neumann
Email: frank.neumann@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Computer Science, Machine Learning, Mathematics, Algorithms, Cyber Security
Project Summary: In the realm of modern warfare, understanding the military applications and intentions of networks is of paramount importance.
This research proposal focuses on employing ML agents to determine the military application of networks based on low-level cyber observables, such as packet data, and incorporating Natural Language Processing (NLP) techniques for the analysis of documents and communications. By combining advanced technology with network analysis and linguistic understanding, this study aims to contribute to enhancing military intelligence and strategic decision-making.
Industry Partner: DEWC Services
University: University of Adelaide
Academic Supervisor: Professor Frank Neumann
Email: frank.neumann@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Computer Science, Machine Learning, Mathematics, Algorithms, Cyber Security
Project Summary: Silicon CMOS quantum dots are one of the most promising candidate technologies for realising the large scale quantum computers needed for fault-tolerant quantum information processing. At Diraq we are looking for enthusiastic and talented students to tackle new engineering challenges and accelerate the development of CMOS qubits.
This project involves advancing understanding of CMOS multi-qubit systems through building theoretical models composed of interacting electrons, including a complete description of spin-spin coupling through advanced electronic structure methods. This will be completed through using existing and developing new bespoke code utilising a variety of theoretical and statistical analysis techniques. The candidate will be working closely with both theory and experimental teams to validate their findings against measurement data.
Industry Partner: Diraq
University: UNSW Sydney
Academic Supervisor: Dr. Andre Saraiva
Email: a.saraiva@unsw.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Electrical Engineering, Computer Science, Physics
Project Summary: This project aims to improve on existing algorithms for SLAM with a view to implementation on Edge AI enabled devices installed on real drones. The desired outcome is a method to intelligently fuse sensors like LiDAR, cameras and inertial systems to provide robust obstacle avoidance capability in cluttered environments without reliable GPS data.
The project will be conducted in both simulation and on real UAV platforms with hardware-in-the loop experiments. Algorithms will be developed for Edge AI enabled devices to achieve UAV payloads that are lightweight, use minimal power whilst meeting flight safety requirements.
Industry Partner: Geodrones Australia Pty. Ltd.
University: UNSW Canberra
Academic Supervisor: Professor Matt Garratt
Email: m.garratt@unsw.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Artificial Intelligence (AI), Mechanical Engineering, Robotics, Electrical Engineering, Software Engineering
Project Summary: Current systems for landing UAVs on ships tend to be GPS or radar based and do not use passive sensing. This project aims to develop a visual guidance solution for maritime launch and recovery of UAVs which is not reliant on specialist hardware on the ship and is resilient to failed or degraded GPS. Moreover, this project will target small vessels that otherwise would not have a UAV capability owing to the lack of infrastructure that can be installed and the significant ship motion caused by operations in anything but calm seas. This will enable UAV support of operations from small uncrewed vessels as well as larger crewed vessels.
The project will investigate the best way to achieve a visually guided approach and landing on to the moving deck of a maritime vessel. It will compare classical machine vision and deep learning approaches to tracking the ship and deck markings with consideration of fusing other sensor modalities such as LiDAR. Use of ship motion prediction via machine learning will also be explored to decide the best time to conduct launch and recovery and to enhance the smoothness of the landing trajectory.
Industry Partner: Geodrones Australia Pty. Ltd.
University: UNSW Canberra
Academic Supervisor: Professor Matt Garratt
Email: m.garratt@unsw.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Mechanical Engineering, Computer Science, Electrical Engineering
Project Summary: Swarms of UAVs are an effective means with which to achieve an objective requiring greater redundancy or greater area coverage than can be provided with a single UAV. However, controlling a large number of UAVs becomes problematic for the operator and places great strain on communication networks. State of the art drone shows are a good example of using large numbers of small drones to great effect but rely on preplanning the trajectories of all the drones before flight to ensure zero collisions and are also reliant on augmented GPS positioning systems to ensure drones fly precise trajectories that do not conflict with one another. This approach does not work in environments having a military context which might be highly dynamic or largely unknown prior to launch and will also not work in GPS degraded environments.
This project aims to solve these limitations by developing neural network architectures capable of outputting control actions which enable a UAV to emulate the sophisticated flocking behaviour of birds. Whilst existing approaches require relative position and velocity of neighbouring UAVs as input, we propose to replace this explicit state information using the raw camera feed from each UAV. The outcomes will contribute to the advancement of communication-free flocking behaviour and pave the way for the adoption of vision-based swarm control in real UAV systems.
Industry Partner: Geodrones Australia Pty. Ltd.
University: UNSW Canberra
Academic Supervisor: Professor Matt Garratt
Email: m.garratt@unsw.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Mechanical Engineering, Computer Science, Electrical Engineering
Project Summary: Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which significantly vary, or are initially contain large uncertainties. In an adaptive control system, the controller parameters are adjusted automatically to compensate for changes in the dynamics of the system and process conditions.
The research project will consist of three stages. The first stage involves developing sophisticated mathematical models for flying hypersonic vehicles taking into account uncertainties and unknown disturbances. The second stage addresses the problem of system identification for flying hypersonic vehicle. It requires applying system identification tools such as nonlinear Kalman filtering, particle filtering and robust extended Kalman state estimation. Edge AI tools such as deep reinforcement learning will also be used. Finally, the third stage will involve developing effective real-time adaptive control algorithms using advanced tools of modern control engineering such as adaptive sliding-mode control, adaptive model predictive control and adaptive H-infinity control. The project will be conducted in both simulation and on real platforms with hardware-in-the loop experiments.
Industry Partner: Lockheed Martin Australia
University: UNSW Sydney
Academic Supervisor: Professor Andrey Savkin
Email: a.savkin@unsw.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Robotics, Computer Vision, Control Systems, Electrical Engineering
Project Summary: Experimental deployment of quantum communication over free space will open a pathway to the secure global quantum internet. In this project you will be involved in developing a prototype quantum communication system that will eventually be used to communicate with low earth orbit satellites.
The project is currently seeking two experimentally focussed PhD students to participate in this work. You will be involved in the development of optical systems that encode entangled photon pairs, development of transmitters for quantum signalling through free space, and in the development of quantum-decoding receivers. You will be joining a large group of students, postdocs and staff at UNSW currently working in this area, both experimentally and theoretically. You can anticipate a world-class education in this exciting new field. As part of your training, you can expect to travel to international conferences as well as receive industry-based training through our key industry partner.
UNSW is a world-leader in the field of quantum technology, and the School of Electrical Engineering and Telecommunications, which you will join, has recently initiated the world’s first bachelor’s degree in quantum engineering. The positions are open to candidates who possess, or hope to possess soon, a First-Class Honours Degree in Electrical Engineering, Physics, or a closely related discipline.
Industry Partner: Northrop Grumman Australia
University: UNSW Sydney
Academic Supervisor: Professor Robert Malaney
Email: r.malaney@unsw.edu.edu
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Electrical Engineering, Physics
Project Summary: This project explores autonomous swarms for detection of underwater objects using decentralised decision support systems and signal detection using machine learning. Sensing, decision, and control of maritime systems are a complex set of tasks due to challenging ocean environments. There is a growing trend towards supporting or replacing high-value crewed vehicles with low-cost autonomous/un-crewed vehicles which can be deployed in greater numbers. In the maritime environment, one particular advantage of using swarms of un-crewed vehicles is to achieve multiple sensing pathways, which can improve sensing performance (through both redundancy and sensor fusion) and allow more flexible decision support systems. However, there are also challenges associated with the communications and control systems required for such multi-agent systems.
The project aims to investigate multi-agent strategies for swarms of maritime vehicles, to investigate opportunities defined by: distributed array sonar-like sensing algorithms for sensing underwater objects and reducing noise exposure to the environment; optimal control strategies for locating units within the maritime environment to improve sensing performance; as well as, multi agent sensor fusion for enhanced autonomous marine navigation, situation awareness, decision making, communication and coordination.
The project is also expected to produce a generalised multi-agent simulation tool, which will include customisable physics components allowing sensing, communications, and control systems models to be tested via monte carlo simulation approaches.
Industry Partner: Saab Australia
University: The University of Adelaide
Academic Supervisor: Dr. Will Robertson
Email: will.robertson@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Machine Learning, Mathematics, Computer Science, Data Science
Project Summary: This research project aims to model threats posed by drone swarm and to devise counter measure to protect major assets such as airports. A drone swarm countermeasures simulator has been developed in Saab Australia. The current capabilities of the simulator include ability to setup various scenarios for asset protection with parametrised sensor models and the ability to model spatially distributed sensors (i.e., sensors mounted on autonomous vehicles that are deployed from protected assets).
The simulator can also model multiple incoming enemy drones with some basic patterns/behaviour. Using and extending the capability of the simulator, we would like to conduct research on the behavioural side of this problem. Consider a scenario where there are a number of friendly (blue team) and malicious (red team) drones competing with each other with different objectives that are not known to each other.
We would like to investigate the problem where the red team and blue team are continually trying to outperform each other reactively. Recently, game theoretic approaches have been used for modelling this problem we would like to review literature in this area and build prototypes demonstrating the effectiveness of the proposed algorithm/approach. We would also like to analyse the proposed algorithm against several performance metrics such as computational efficiency and scalability as the number of drones increase. Such analyses also include assessing the algorithm’s robustness against graceful degradation in adverse conditions such as communication failure/reduction in bandwidth, weather induced limitations, etc.
Industry Partner: Saab Australia
University: The University of Adelaide
Academic Supervisor: Dr Mingyu Guo
Email: mingyu.guo@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Computer Science, Robotics
Project Summary: Bi-static and Multi-static localisation use time differences of arrival between multiple receivers to localise objects being tracked by active sonar. The aim of this project is to develop a multi-static deinterleaver algorithm that consistently assigns detection events to real-world tracks, in order to execute bi-static and multi-static geometric calculations. Solving this problem is expect to require a combination of tracker development, statistical methods, machine learning and hardware-accelerated brute-force computation.
Multistatic active localisation will contribute to the anti-submarine warfare capability of RAN surface combatants, particularly in the face of rapidly decreasing submarine noise levels that may soon make passive acoustic surveillance less effective.
Industry Partner: Saab Australia
University: The University of Adelaide
Academic Supervisor: Dr. John Maclean
Email: john.maclean@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD), Master of Philosophy (MPhil)
Relevant discipline areas: Mathematics, Statistics, Computer Science
Project Summary: Target Motion Analysis (TMA) is the process of estimating the two-dimensional location of a moving platform, using a temporal sequence of one-dimensional (bearing) observations from a moving sensor. Doppler (range-rate) information may also be available. TMA is used for passive acoustic surveillance and is therefore a core function in anti-submarine warfare, whether conducted by submarines or surface combatants. TMA solutions are inherently ambiguous under an assumption of straight-line movement, therefore manoeuvre is used by the sensor operator to resolve ambiguities, and by the tracked target to mislead TMA processes being conducted by adversaries.
Industry Partner: Saab Australia
University: The University of Adelaide
Academic Supervisor: Dr. John Maclean
Email: john.maclean@adelaide.edu.au
Offered for: Master of Philosophy (MPhil)
Relevant discipline areas: Data Science, Mathematics, Software Development
Project Summary: Designing materials with advanced functionalities is the focus of contemporary quantum materials science. In this sense, one of the most fascinating goals in the field is the search for novel materials able to perform as to conventional low critical temperature superconductors but that manifest the exceptional properties of a superconductor at much higher temperatures. The reason for this is clear: higher critical temperatures imply superconductivity properties at much higher temperatures and hence being able to handle, measure and access a macroscopic quantum mechanical state without the limitations associated with extremely complicated cryogenics systems. Interestingly, although the current research in superconductor design is dominated by conventional (phonon-mediated) superconductors, there seems to be a widespread consensus that achieving higher critical temperature of operation may require the introduction of different pairing mechanisms.
These two-facts combined are telling us that superconductors with higher critical current would not only pave the road for a wide range of technological applications, affecting strategic areas, such as quantum sensing for defence and medical applications, that in present times limits the use of superconductors due to their very hard conditions of access, but this will also open exciting avenues for fundamental physics.
Industry Partner: Silanna
University: University of Adelaide
Academic Supervisor: Dr. Giuseppe Tettamanzi
Email: giuseppe.tettamanzi@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD), Master of Philosophy (MPhil)
Relevant discipline areas: Mathematics, Computational Physics, Electromagnetic Mechanics, Quantum Mechanics, Physics, Materials Engineering, Chemical Engineering
Project Summary: Leaning on the zero resistance properties of superconductors materials, superconducting technology has garnered considerable theoretical and practical interest, with applications spanning the areas of quantum computing, ultra-high precision sensing and quantum metrology. The key phenomenon underpinning these sectors is the Josephson effect, which is the ability for quantum tunnelling super-current to flow between two superconducting electrodes. This effect has been exploited to construct Superconducting Quantum Interference Devices (SQUIDs), which can be used as state-of-the-art sensors of electromagnetic (EM) signal.
More recently, several new kinds of SQUID devices have demonstrated a great potential for Defence/medical applications such as, for example, the task of capturing and analysing signals used for communications. So far, circuit models have been used to model the performances of these devices, however these are somehow limited. Hence, by using new effective field theories for superconductivity such as the phenomenological Ginzburg-Landau formalism or the non-equilibrium statistical mechanic’s approaches, this project will develop and implement a new class of microscopic models. This in turn can be used to validate the behaviour of more complicated devices.
Industry Partner: Silanna
University: University of Adelaide
Academic Supervisor: Dr. Giuseppe Tettamanzi
Email: giuseppe.tettamanzi@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD), Master of Philosophy (MPhil)
Relevant discipline areas: Mathematics, Computational Physics, Electromagnetic Mechanics, Quantum Mechanics, Physics, Materials Engineering, Chemical Engineering
Project Summary: Over the past several decades, near earth orbits have become increasingly congested as the number of space borne applications grow. There are strong national security and commercial imperatives to develop capabilities for obtaining situational awareness in space. Radar is a mature sensing techniques that can be used in the space domain. Passive radars exploit emitters of opportunity, such as terrestrial broadcasts, to provide the transmitted signals. The subsequent low probability of discovery is particularly attractive for surveillance. However, the lack of control over transmit waveform raises technical challenges for detection and classification. This project will explore novel signal processing approaches to address these challenges. Silentium Defence’s passive radar infrastructure is capable of capturing vast quantities of real data, which will serve as a critical ingredient in the research.
Industry Partner: Silentium Defence
University: The University of Adelaide
Academic Supervisor: Associate Professor Brian Ng
Email: brian.ng@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD), Master of Philosophy (MPhil)
Relevant discipline areas: Radar signal processing, Machine Learning, Physics
Project Summary: In military terms, electronic support (ES) is the branch of electronic warfare (EW) related to the collection and analysis of electromagnetic signals in the environment to identify and inform decision makers/users for situational awareness.
The aim of this project is to develop a novel airborne ES sensor capable of operating in a complex congested Radio Frequency (RF) environment. The sensor will consist of a broadband antenna array, RF front end and digital backend. The candidate(s) will develop novel antenna designs with Direction Finding (DF) functionality, integrating the designs to build a complete sensor device. They will also be exposed to cutting edge algorithms for emitter DF, and signal parameterisation and characterisation.
Industry Partner: SRC Aus Pty. Ltd.
University: University of Adelaide
Academic Supervisor: Dr. Nghia Nguen Trong
Email: nghia.nguyentrong@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Electrical Engineering, Computer Science, IT, Software Engineering, Telecommunications, Mathematics
Project Summary: The aim of this PhD project is to develop novel machine learning algorithms and adaptive methods to automatically respond to the constantly changing and complex Radio Frequency (RF) environment in electronic warfare (EW). The project will involve the design, implementation, and testing of new machine learning techniques to identify and predict the characteristics and behaviour of RF emitters, in order to improve the performance of EW receiver processing.
Industry Partner: SRC Aus Pty. Ltd.
University: The University of Adelaide
Academic Supervisor: Dr. Feras Dayoub
Email: feras.dayoub@adelaide.edu.au
Offered for: Doctor of Philosophy (PhD)
Relevant discipline areas: Electrical Engineering, Electronic Engineering, Mechanical Engineering, Computer Science, IT Engineering, Software Engineering, Telecommunications, Robotics, Mathematics
