Postdoctoral Research Associate in Mathematics AI – University of Sydney
Location: Town of Yorktown
Overview
Full time, 2 years fixed term. Located at the School of Mathematics and Statistics, Camperdown Campus
The School of Mathematics and Statistics at the University of Sydney is currently seeking a Postdoctoral Research Associate in Mathematics for Trustworthy AI to work on stochastic optimisation and statistical theory for deep representation learning, fair machine learning, privacy-preserving learning, multi-objective learning, and AI Safety. There will be potential opportunities to visit the IBM research center in Yorktown heights New York, to conduct collaborative research related to trustworthy AI.
- Opportunity to contribute to research in mathematics for trustworthy AI at the University of Sydney
- Base Salary Level A, $105,314 – $116,679 p.a. + 17% superannuation
Your key responsibilities will be to:
- design efficient stochastic optimisation algorithms for trustworthy machine learning, deep representation learning, multi-objective learning, and AI Safety
- work on statistical generalization theory for optimisation algorithms and the intricate interaction between statistics, computational optimization, and trustworthy elements (e.g., –fairness, differential privacy, and robustness)
- conduct extensive experimental validation of the developed learning algorithms and carry out industrial applications
- contribute to external engagement and academic communications.
The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance of recruiting talent aligned to these values and are looking for a Postdoctoral Research Associate in Mathematics for Trustworthy AI who has:
- a PHD (or near completion) in mathematics, applied mathematics, data science, statistics, or a related area
- an excellent track record of publishing high-quality papers on machine/deep learning, machine learning theory or optimisation in top-tier venues
- research experience in machine learning and related areas
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