PhD Scholarship on Adversarial Machine Learning

PhD Scholarship on Adversarial Machine Learning @ UNSW Sydney and CSIRO's Data61

The use of machine learning models has become ubiquitous. Their predictions are used to make decisions about healthcare, security, investments and many other critical applications. These models are widely used in many cyber defense systems for network security operations, malware analysis, etc. But despite the many successes, the very property that makes machine learning desirable: adaptability, is a vulnerability that may be exploited by an attacker that could potentially result in the severe degradation of the integrity, security, and performance of cyber defense systems. All machine learning systems are trained using datasets that are assumed to be representative and valid for the subject matter in question. However, malicious actors can impact how the artificial intelligence system functions by poisoning the training data. This threat is exacerbated when the machine learning pipeline that includes data collection, curation, labeling, and training is not controlled completely by the model owner. This project will focus on understanding, evaluating, and improving the effectiveness of machine learning methods in the presence of motivated and sophisticated adversaries.

The student will be supervised by a capable team including academics from UNSW and researchers from CSIRO's Data61. There may be an opportunity to engage with industry partners. 

We are looking for a candidate who is currently based in Australia and can commence their studies in Term 2 2021 (May/June 2021). The scholarship is open to both domestic and international students. International applicants should be able to secure a Tuition Fee Scholarship from UNSW. This entails an academic record that is equivalent to an Australian First Class Honours degree in Computer Science from a reputed institute. A publication track record in machine learning and/or security is highly desirable.

The scholarship includes: 

-$37K per annum (tax-free) for a period of 3 years; extendable up to 6 months 
-Access to operational budget for research support and conference travel
-Mentoring and training opportunities through Data61

Interested candidates should send their CV and academic transcripts to Professor Salil Kanhere ( and Dr. Surya Nepal (

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