Physics Informed Neural Networks to build a superior biofluids solver

This PhD is an exciting opportunity for a highly motivated candidate with academic ambitions. The project aims to develop a new suit of biofluids solver as a significant advance for medical diagnostics and device development.

During this PhD the successful candidate will develop physics-informed neural networks to accurately predict fluid dynamics in large generalised vascular networks such as the coronary artery tree or the Circle of Willis.

The right candidate will work closely with a multi-disciplinary team across medicine, computer sciences and mechanical engineering. Relevant previous research experience is desired. The right candidate must be an experienced Python user and should have worked with neural networks before. A good fluid dynamic understanding, especially for biological systems, and a strong mathematical understanding are preferable.

Scholarship

• $15,000 Top Up Scholarship for a suitable applicant who is awarded a UNSW Research Training Program Scholarship, Research Training Program International Scholarship, University Postgraduate Award or University International Postgraduate Award.  

Eligibility

• Domestic and International applicants
• PhD only
• Must be awarded a UNSW Research Training Program Scholarship, Research Training Program International Scholarship, University Postgraduate Award or University International Postgraduate Award
• Must be an experienced Python user who has worked with neural networks before
• A good fluid dynamic understanding, especially for biological systems, and a strong mathematical understanding are preferable.

How to apply

Please apply with your CV stating your final GPA or WAM for all degrees, references and a cover letter addressing all the selecting criteria to Dr Susann Beier at s.beier@unsw.edu.au. 

If you are shortlisted, you will need to submit a formal application to UNSW for a UNSW research scholarship.