- Salary Package: From $93,830 - $100,559 (Level A step 6-8) p.a., plus 13.25% employer's superannuation and annual leave loading.
- Appointment Type: Full time, fixed term position for 1 year (possible extension).
- Macquarie University (North Ryde) location
We are seeking an enthusiastic Postdoctoral Research Fellow to participate in a new research project on privacy-enhanced machine learning with health data. The goal of this project is to develop and evaluate a new generation of private machine learning methods, to support clinicians and health informaticians in making better healthcare decisions. The research will focus on unique genomic and proteomic datasets facilitating personalised diagnosis and treatment decisions.
You will be working within a cross-disciplinary Human-Centric Security research program including highly experienced and internationally recognised researchers from the Centre for Health Informatics, Australian Institute of Health Innovation (AIHI), and Optus-Macquarie Cybersecurity Hub.
You will bring your expertise in machine learning into a multi-disciplinary team and help guide the team to develop real-world systems. We are looking both for academic rigour and outputs, as well as pragmatic analytical skills and problem solving. Ideally, you will have the desire to research and develop health/medical applications of machine learning, with experience in modern privacy-preserving methods for real-world applications and working in a collaborative setting. You will be a good communicator, passionate about your research, and enjoy working in a dynamic team.
The Centre for Health Informatics (CHI) explores the application of novel digital technologies in healthcare with a focus on Artificial Intelligence and its implications for transforming models of care, driving system-wide changes and delivering personalised healthcare. The Optus Macquarie University Cyber Security Hub is an interdisciplinary network launched in August 2016 by Macquarie University with Optus as the founding partner.
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About the Project
This project will focus on the development and evaluation of privacy-preserving machine learning methods for membership inference attacks applicable to high-dimensional health data. We will study the state-of-the-art membership inference methods and assess their performance on proprietary sets of genomic and transcriptomic data. Then, we will devise an innovative privacy-preserving membership inference method for health data. We will benchmark the performance of the new methods, analyse their use beyond the considered datasets, and publish our findings as research publications. The expected outcomes of the project are a knowledge transfer workshop with industry, new privacy-preserving methods, and scholarly research publications.
To be considered for this position, please apply online by submitting your full CV including publication list and Cover letter addressing the selection criteria (2 pages max.). We also request a one-page summary of your top-3 publications explaining (a) why these publications were important, and (b) what was your part in these publications. (Applications not providing these 3 documents will not be considered.)
- PhD in Computer Science, Data Science, Statistics, or a closely related field.
- Proven experience in Machine Learning, Artificial Intelligence, or Data Science research, and exceptional analytical skills.
- Excellent research track record evidenced by publications in top-tier conferences and journals, like JMLR, IJCAI, KDD, ICML, etc.
- Proven experience with relevant coding and data analytics environments and language, used to develop practical applications.
- High-level oral/written communication skills and demonstrated ability to work in a multi-disciplinary collaborative team.
- Understanding of privacy technologies and/or health applications.
- Experience in liaising with internal/external stakeholders and working on collaborative projects.
Specific Enquiries: A/Prof. Shlomo Berkovsky at email@example.com
General Enquiries: HR Administrator, Rohana Nagarajah at firstname.lastname@example.org
Applications Close: Sunday 16 August 2020 at 11:55pm AEST
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