Griffith University Variational Decentralised Learning PhD Scholarship 2027

Candidates are expected to have solid foundations in statistics, information theory, optimisation methods, and demonstrated research outputs (quality publications) and programming skills.

The PhD study aims to originate variational decentralised learning theories and methods to integrate variational, decentralised and deep learning to satisfy complex stylistic, local-global integrative requirements.

The scholarship will sponsor foundational research on variational decentralised learning, including

• characterising distributional discrepancy and vulnerability
• quantifying decentralised non-IIDnesses
• modelling stylistic uncertainties
• transforming existing averaging-aggregation learning frameworks.

• Table of Content
• About Griffith University
• Variational decentralised learning PhD Scholarship
• Aim and Benefits of Variational decentralised learning PhD Scholarship
• Requirements for Variational decentralised learning PhD Scholarship Qualification
• Application Deadline
• How to Apply

Variational decentralised learning PhD Scholarship

Aim and Benefits of Variational decentralised learning PhD Scholarship

The scholarship comprises:

• a tuition fee offset/scholarship
• a living allowance stipend.

The value of each stipend scholarship is $40,900 per annum (full time, indexed) for three years.

Requirements for Variational decentralised learning PhD Scholarship Qualification

• This scholarship is available to eligible candidates to undertake a direct entry three-year PhD program.

Application Deadline

How to Apply

Before submitting your application, submit an expression of interest (EOI) to Professor Longbing Cao at [email protected].

Your EOI should include:

• selected publications
• your CV
• other evidence of research foundations and achievements.

Candidates without solid foundations and demonstrated research outputs will not be eligible for the scholarship.

For more information, kindly visit Griffith University scholarship webpage.