Digital learning of particle-laden flows (PhD scholarship)

Applications close

30 June 2026

• You'll receive a stipend of $37,010 per annum for a maximum duration of 3.5 years while undertaking a QUT PhD. The duration includes an extension of up to 6 months if approved for your candidature. This is the full-time, tax-exempt rate which will index annually.
• You will receive a tuition fee offset/sponsorship, covering the cost of your tuition fees for the first 4 full-time equivalent years of your doctoral studies.
• As the scholarship recipient, you will have the opportunity to work with a team of leading researchers, to undertake your own innovative research in and across the field.

• You need to meet the entry requirements for a QUT Doctor of Philosophy , including any English language requirements.
• Enrol as a full time, internal student (unless approval for part-time and/or external study is obtained).
• Completion of a bachelor (honours) degree or master’s degree by research in any of the following:
  • mechanical engineering
  • applied mathematics
  • civil engineering
  • geotechnical engineering
  • applied physics
  • other related disciplines.
• Demonstrated knowledge or experience in any of the following:
  • computational fluid dynamics
  • applied mathematical modelling
  • machine learning
  • multi-fidelity modelling
  • numerical methods.
• Demonstrated programming ability (MATLAB/Python/C++) and enthusiasm to learn PyTorch.
• Previous experience in one of Nek5000, Basilisk, or OpenFOAM desirable.

• The first step is to email Dr Tony Zahtila briefly detailing your academic and research background, your motivation to research in this field and interest in this scholarship. Please include your CV.
• Shortlisted candidates will be invited to interview.
• If you are nominated as our preferred candidate, you will then be invited to submit an Expression of Interest (EOI) following the advice at How to apply for a research degree .

A PhD position is available in the School of Mechanical, Medical, and Process Engineering at the Queensland University of Technology (QUT, Brisbane, Australia) related to machine learning for particle laden fluid mechanics.

QUT is a major Australian university with a global outlook and a 'real world' focus. We are one of the nation’s fastest growing research universities. The student will be part of the Faculty of Engineering. This project will involve international collaboration and will be supervised by Dr Tony Zahtila .

Particle-laden flow is a ubiquitous phenomenon and relevant to many engineering processes, ranging from large-scale environmental and industrial transport to microscale applications such as aerosol delivery, spray dynamics, and particulate manipulation in confined devices. This project will target scientific machine learning strategies such as through autoencoders and neural differential equations on existing data. This project will also involve the generation of new data in emerging applications.

As a PhD student, you will address the forecasting challenge of how to predict and reconstruct the evolution of particle-laden turbulent flows from limited data using scientific machine learning. You will also be involved in the data-generation and curation for model development. Accordingly, you will gain access to the high-performance computing (HPC) facilities at QUT.