Sort by
Refine Your Search
-
Category
-
Program
-
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
-
through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
-
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
-
queries, and automating data transformations. By combining advancements in natural language understanding, algorithm synthesis, and debugging, the proposed framework will enable developers to efficiently
-
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
-
explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
-
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
-
This project focuses on developing algorithms capable of automatically identifying and categorizing mobile ringtones. This involves leveraging machine learning techniques to analyze audio signals
-
We are excited to offer a fully funded PhD position at the Faculty of Engineering, Monash University (Australia). This project focuses on developing new algorithms to equip social robots with
-
the headspace website. Possible approaches to addressing this challenge might include: Developing algorithms to identify patterns and preferences based on service users’ previous content engagement