Sort by
Refine Your Search
-
Listed
-
Employer
- Monash University
- Victoria University
- Macquarie University
- University of Tasmania
- Flinders University
- Deakin University
- RMIT University
- Curtin University
- MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA
- UNIVERSITY OF SYDNEY
- University of Adelaide
- Australian Catholic University
- RMIT UNIVERSITY
- Swinburne University of Technology
- The University of Western Australia
- University of New South Wales
- University of Sydney
- University of Western Sydney
- CSIRO
- Queensland University of Technology
- Southern Cross University
- James Cook University
- FLINDERS UNIVERSITY
- Nature Careers
- The University of Queensland
- UNIVERSITY OF WESTERN AUSTRALIA
- University of Wollongong
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Australian National University
- Charles Sturt University
- Edith Cowan University
- Federation University Australia
- La Trobe University
- MONASH UNIVERSITY
- University of New England
- University of South Australia
- University of Southern Queensland
- 27 more »
- « less
-
Field
-
for intelligent systems to guard their execution and evolution. Required knowledge - deep learning - software development lifecycle
-
In recent years, AI techniques such as GANs and associated deep learning neural networks have become popular tools applied to the production and creation of works of art. In 2018, AI Art made
-
techniques. It may also include hardware development of wearable assistive devices that use audio and haptic feedback. Required knowledge Image processing Computer vision Deep learning Programming (Python, C
-
for monitoring and controlling the brain with medical devices and imaging brain activity in new and important ways. Required knowledge Statistical signal processing, Statistical Inference, Machine learning, Deep
-
The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
-
the simple equation that more training data = better performance. Learning—in particular, the advanced deep learning methods, like BERT for NLP and ResNet for image processing—often require thousands
-
unreliable techniques and is thus often not done so that infected colonies are discovered far too late. In this project, we aim to build Ai tools based on Deep Learning to automatically classify the health
-
context for monologue and multi-party bilingual dialogue translation [1,2, 3], capitalizing on the flexibility and expressive power of deep learning and neural networks. In this project, we will push the
-
for the relevant attributes or properties. General composition mechanisms will be learned such that applications can combine appropriate components to generate desired data. For example, the script of an emotion
-
addition to audio. Candidates will be expected to devise novel multi-modal generation models by incorporating ideas and techniques from various techniques, such as causality, deep learning, deep reinforcement