$6.2 million has been awarded to the Next Generation Emerging Technologies Graduates program and $4.2 million for the Next Generation Artificial Intelligence Graduates program. The Next Generation Graduates programs will co-fund student scholarships with each program's industry and university partners. These programs will allow students to be part of a multi-disciplinary team aimed at solving a real-world challenges.
The programs will provide scholarships to domestic students (Australian Citizens and Permanent Residents) only.
CSIRO is an Australian Government corporate entity, with a Board and Chief Executive. They are constituted by and operate under the provisions of the Science and Industry Research Act 1949, which sets out our functions and powers, as well as those of our Minister, Board and Chief Executive. The governance, performance and accountability of our operations, including the use and management of public resources are set out in the Public Governance, Performance and Accountability Act 2013 and related rules.
PhD, Masters 1 year project (RTP eligible), Coursework Masters (not RTP eligible), Honours
Successes of machine learning have catalysed a revolution across the spectrum of human endeavours. Enterprises offer machine learning services to improve their businesses and provide ready-made intelligence for end-user applications. However, there is an increasingly striking discordance between its rapid growth and renewed awareness of privacy protection in this era of intelligence. Individual data often carry private information and models are deemed as intellectual properties.
The security of private and sensitive information carried by the data and machine learning models that could be potentially exposed must be guaranteed. This program aims to nurture Australian next-generation workforce in the field of Privacy-preserving Machine Learning (PPML). PPML aims to protect the privacy of data or models used in machine learning, at training or inference time and during system deployment. It is the key enabler of trustworthy ML systems and benefits both model users and model owners.
We plan to explore emerging privacy-enhancing techniques for realising PPML systems, address practical challenges during deployment, and adopt PPML systems to Australian prioritised industry sectors. The outcome expects to pave a long-sought lane towards broad market adoptions of PPML and be an integral part of the next generation Australian digital economy and society.
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