The MARCS Institute
Lead Researcher: Doctor Saeed Afshar
About the project
We invite applications from highly motivated graduates seeking to undertake a Doctor of Philosophy (PhD) degree to work with Neuromorphic Engineering pioneer, Professor André van Schaik, in one of the world’s leading neuromorphic engineering research labs, on a project to design a Neuromorphic processor that dynamically learns the normal operating states of an edge device and detects, prevents and reports anomalous behaviour and communications.
Smart edge devices are becoming ever more ubiquitous in defence environments. As the functional complexity of these devices increases, so does the likelihood of security vulnerabilities. The conventional approach to cyber security for edge devices such as remote autonomous vehicles involves the collection and transmission of sensory, actuator and network data for inspection by a centralized high-performance computing system. This approach, of gathering and transmitting large quantities of data from the edge to a central node for processing, requires continuously reliable high bandwidth communication channels as well as costly local storage and transmission capabilities at the edge device. As the number of edge devices and their information gathering and complexity increase, this model of information processing becomes ever less sustainable.
Agent based anomaly detection systems are still in an early stage of development with the state-of-the-art solutions relying on computationally expensive Machine Learning (ML) solutions which limits their use in edge applications.
In this project, you will first survey the current state of the art anomaly detection systems in the Neuromorphic and Machine Learning fields. Building on these solutions, you will design and test novel low-power high speed spiking neural network architectures which can build internal models of normal edge device behaviour with respect to sensory and network input and detect anomalous behaviour against this background model. By only transmitting anomalies and through learning to respond locally to anomalies, the system could dramatically reduce the burden on the available communication channels and human operators while rapidly dealing with normal signal traffic at the edge. After simulation on a conventional processor, your developed architecture must be implemented in FPGA hardware and be put to the test on real-world autonomous platforms in live trials against expert hackers.
What does the scholarship provide?
- Domestic candidates will receive a tax-free stipend of $30,000(AUD) per annum for up to 3.5 years to support living costs, supported by the Research Training Program (RTP) Fee Offset.
- International candidates will receive a tax-free stipend of $30,000(AUD) per annum for up to 3.5 years to support living costs. Those with a strong track record will be eligible for a tuition fee waiver.
- Support for conference attendance, fieldwork and additional costs as approved by MARCS.
- Access to the extensive range of MARCS specialised equipment and laboratory facilities.
- Additional funding to support training and equipment purchases.
- A rich environment of support and academic expertise via supervisory panels, seminar, colloquia, international and industry collaborations.
International candidates are required to hold an Overseas Student Health Care (OSHC)(opens in new window) insurance policy for the duration their study in Australia. This cost is not covered by the scholarship.
This scholarship is funded by the Department of Defence(opens in new window). To be eligible for this scholarship, the applicant must comply with the following:
- be an Australian citizen, or a citizen of the United Kingdom, New Zealand, United States of America or Canada.
- must meet specific Defence related requirements in relation to citizenship and possible clearances.
- if selected, be able to complete a schedule 17 - Student Participation and Intellectual Property (IP) deed in relation to this activity from the Defence Science Partnership Deed and agreements.
In addition, the successful applicant should:
- hold qualifications and experience equal to one of the following (i) an Australian First Class Bachelor (Honours) degree, (ii) coursework Masters with at least 25% research component, (iii) a Research Masters degree, or (iv) equivalent international qualifications in electrical engineering, or computer science.
- have demonstrated research experience in neuromorphic engineering.
- have a background in signal processing with strong programming skills in Python, Matlab, and/or C++, and an interest in signal processing in biology.
- be enthusiastic and highly motivated to undertake further study at an advanced level.
- not hold a degree of the same, or higher level, as the proposed candidature.
- reside in Australia for the duration of their studies, except for periods of approved overseas study leave.
- not receive income from another source to support general living costs while undertaking the program, if that income is greater than 75% of the stipend rate.*
* The 75% rule referenced above does not apply to: a) income earned from sources unrelated to the research or b) income related to the research but not for the purpose of supporting general living costs.
Desirable skills include:
- demonstrated expertise using Verilog or VHDL for FPGA programming.
- experience in experimental design.
- experience with mixed-signal integrated circuit design.
- proven ability to work successfully as part of a team.
- familiarity with machine learning and machine learning concepts.
International applicants must demonstrate English language proficiency.(opens in new window)
How to apply
Follow the step-by-step instructions on the how to apply for a project scholarship(opens in new window) page.
- Note: You do not need to complete 'Step 5: Submit an online application for admission' when applying for this scholarship. You must complete 'Step 6: Submit an online application for a project scholarship'.
Incomplete applications or applications that do not conform to the above requirements will not be considered.
For questions and advice about the research project, please contact the Lead Researcher;
Doctor Saeed Afshar: email@example.com
For questions and advice about the application process, please contact the Graduate Research School: firstname.lastname@example.org.
Applications close 31 August 2022
*Applications close at 11.59pm Australian Eastern Standard Time (AEST).
Scholarship reference code: PS2021_18_ICNS