Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Monash University
- The University of Queensland
- University of Adelaide
- Curtin University
- University of Sydney
- CSIRO
- Nature Careers
- Queensland University of Technology
- University of Southern Queensland
- La Trobe University
- RMIT University
- UNIVERSITY OF WESTERN AUSTRALIA
- University of New South Wales
- Australian National University
- RMIT UNIVERSITY
- The University of Western Australia
- UNIVERSITY OF ADELAIDE
- UNIVERSITY OF SYDNEY
- University of Technology Sydney
- 9 more »
- « less
-
Field
-
automated recovery algorithms, improving system resilience. Research Areas for Master’s and PhD Students AI-Enhanced Resource Forecasting and Optimization: Research Focus: Developing and testing ML algorithms
-
fixed-term appointment Remuneration: 4-year scholarship package totalling approximately $47,000 per annum tax exempt (2025 rate) 4-year Project Expense and Development package of $13,000 per annum
-
domains for applications to the mining industry and beyond. We have an opportunity for a passionate and skilled research scientist to design and develop cutting-edge algorithms and fused sensor systems
-
, and evaluation of advanced software engineering techniques and methodologies aimed at detecting, mitigating, and preventing misinformation online. The successful candidate will develop novel AI-driven
-
formula is true or false (EXPTIME vs NP). Can we develop and implement efficient algorithms for this problem? This problem has been attacked using multiple different methods for the past 40 years, without
-
developed and implemented such methods for a plethora of non-classical logics [2]. But how can we guarantee that the implementation is faithful to the theory? Indeed, how can we be sure that we have not made
-
This project aims to develop robust algorithms capable of identifying and analyzing fingertips extracted from both static images and video footage. Machine learning techniques, particularly computer
-
experiments for months before the value of output y is measured for some given input x. This creates an exciting challenge for AI researchers to develop smart algorithms that can find the optimal value of input
-
and polyploid crop species and benchmark them against other methods such as graph-based methods. This project will combine algorithm development and computational programming with large population
-
queries, and automating data transformations. By combining advancements in natural language understanding, algorithm synthesis, and debugging, the proposed framework will enable developers to efficiently