Deep variational learning for complex uncertain data
Enterprise data present increasing significant characteristics and complexities, significantly challenging the SOTA probabilistic and deep learning systems.
This research aims to develop novel and systematic deep variational learning theories and models for analytically explainable, complexity-resilient enterprise analytics.
Key details
- 20246179, 20246756
- PhD
- Applications close on 30 April 2025 at 11pm
- Domestic
- Information technologies, science
- $38,500 p.a. (2025 rate)
In the era of big data, digital innovation increasingly relies on understanding data complexities. Automated scalable analytics of enterprise data complexities become increasingly imperative for small-to-large enterprise innovations to address non-conventionally solvable problems by existing learning systems and vendor solutions.
About the scholarship
The students will be intensively supervised by internationally active research leaders in the areas of data science and AI (DSAI, including statistical, deep/machine learning) for cross-disciplinary theoretical and empirical innovations on deep variational learning, which will synergise advanced statistical learning with deep neural innovations for complex and uncertain data.
The students will join an internationally active DSAI research lab with intensive research culture.
The research is expected to produce high quality research outputs that transform existing statistical learning and deep learning. The outcomes are expected to be foundational and applicable to any enterprises for transforming their enterprise analytics capability/capacity, evidence-based transformation, productivity lift and widespread data-intensive decision-making.
Availability
This scholarship is available to eligible candidates to undertake a direct entry three-year PhD.
Award holders may have:
- published papers in well-recognized journals or conferences as the main contributors
- built a substantial foundation in statistics, AI, data science, big data analytics, machine learning, computer vision or robotics.
Components
The scholarship comprises:
- a tuition fee offset/scholarship
- a living allowance stipend.
The value of each stipend scholarship is $38,500 per annum (full-time, indexed) for three years.