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Job Record #16991
TitleLattice Boltzmann-based Reduced-Order Modelling
CategoryPhD Studentship
EmployerQueensland University of Technology
LocationAustralia, Queensland, Brisbane
InternationalYes, international applications are welcome
Closure DateSaturday, May 01, 2021
Project summary: Miniaturisation is one of the most important current drivers 
for chemical and biological processes involved in gas sensing, water 
purification, cell culture and separation, and micro-reactors, among others. The 
lack of fundamental understanding of the microscopic fluid flow mechanisms, in 
particular the behaviour of non-ideal fluid mixtures, is a critical aspect 
underpinning the development of cutting-edge technologies. The present project 
will address the yet-to-be-solved challenges related to the cost of using 
Lattice Boltzmann (LB) approached in complex real-world microfluidic 
applications and devices. The project will develop machine-learning(ML)-based 
approach to create surrogate LB-based Reduced-Order Models that allow for fast 
and reliable numerical predictions, critical for employing the approach to 
realistic microfluidic applications.

This PhD project is part of a research project funded by the Australian Research 
Council (ARC) in collaboration with international experts from Australia, 
Europe, and the US, as well as industry partners.

The PhD student will join an international team of scientists dedicated to 
developing computational modelling for microfluidics and advancing knowledge of 
non-ideal fluid mixture behaviours that are critical for the rational design and 
robust optimisation of microfluidic applications.

Skills & experience:
Demonstrated knowledge and skills relevant to the thesis project and the subject 
of study
Demonstrated knowledge in at least one of the following areas: computational 
Lattice Boltzmann method, statistical/Bayesian methods, machine learning, 
reduced order modelling, and applied and computational mathematical modelling, 
viscoelastic fluid flows.
Demonstrated programming skills (C++, Matlab, Python)
Demonstrated written and oral communication skills with very good proficiency in 
Ability to work independently and to formulate and tackle research problems will 
be critical. Excellent organisational skills, be highly analytical, able to 
multitask under tight time frames will be considered highly.

The provision of a scholarship is conditional on successful application and 
admission to the Doctor of Philosophy course. Eligibility for admission to a 
research degree is determined by the QUT Graduate Research Centre 

Contact details: A/Prof. Emilie Sauret,
Contact Information:
Please mention the CFD Jobs Database, record #16991 when responding to this ad.
NameEmilie Sauret
Email ApplicationYes
Record Data:
Last Modified00:01:11, Wednesday, February 24, 2021

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