Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Monash University
- Curtin University
- University of Sydney
- The University of Queensland
- University of Adelaide
- UNIVERSITY OF SYDNEY
- University of Southern Queensland
- Australian National University
- La Trobe University
- UNIVERSITY OF WESTERN AUSTRALIA
- University of New South Wales
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- BOND UNIVERSITY
- CSIRO
- Macquarie University
- Murdoch University
- Queensland University of Technology
- RMIT University
- The University of Western Australia
- UNIVERSITY OF ADELAIDE
- 10 more »
- « less
-
Field
-
Background in Machine Learning, Algorithms and Data Structures
-
substantial research project, GPA 80%+ from a reputed university Refereed publications including journal or conference of high repute Desirable Background in Algorithms and Data Structures
-
Since the 1990s, researchers have known that commonly-used public-key cryptosystems (such as RSA and Diffie-Hellman systems) could be potentially broken using an efficient algorithm running on a
-
privacy-enhancing techniques such as secure multi-party computation, homomorphic encryption, differential privacy, and trusted execution to design algorithms and protocols to secure ML models within
-
Project description: Nowadays, data-driven machine learning algorithms are well suited to solve real-world problems that require high-level prediction accuracy. However, it seems as if nothing beats
-
headlines around the world when a “work of art created by an algorithm” was sold at auction by Christie’s for $432,500 – nearly 45 times the value estimated before auction. It turned out that the group behind
-
feasibility, and to facilitate the rapid translation of study findings into registry practice and health data environments. Project goals: The aim of the project is to develop cutting-edge AI algorithms
-
on clusters and high-performance computing infrastructure), Information Retrieval methods, Machine Learning algorithms, wrangling large-scale datasets, and showcasing the research results. The ability to work
-
environment. The virtual world runs a little like a computer game, except there are no human players, all the components of the game are computer-controlled by algorithms parameterised from real insect
-
distributions. We wish to represent the biological networks into proper formats, e.g., vector representations, so that existing machine learning algorithms (e.g., support vector machines) can readily be used