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ARC PhD Scholarship – Civil Composites, University of Southern Queensland

Division:

Research and Innovation

Department:

Centre for Future Materials

Classification:

PhD (Higher degree by research) in Civil Composites

Location:

Toowoomba

Date:

15 February 2018

Responsible to:

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PhD Scholarship - Polymer Composites, University of Southern Queensland

Division:

Research and Innovation

Department:

Centre for Future Materials

Classification:

PhD (Higher degree by research) in Polymer Composites

Location:

Toowoomba

Date:

Xuesen Zeng's picture

PhD Scholarship - Civil Composites, University of Southern Queensland

Division:

Research and Innovation

Department:

Centre for Future Materials

Classification:

PhD (Higher degree by research) in Civil Composites

Location:

Toowoomba

Date:

Xuesen Zeng's picture

Research Fellow (Polymer Chemistry), University of Southern Queensland

Division:

Research and Innovation

Department:

Centre for Future Materials

Classification:

USQ Academic Level B

Term:

Full-time fixed term appointment for 2 years

Location:

Xuesen Zeng's picture

Research Fellow (Civil Composites), University of Southern Queensland

Division:

Research and Innovation

Department:

Centre for Future Materials

Classification:

USQ Academic Level B

Term:

Full-time fixed term appointment for 3 years

Location:

Xuesen Zeng's picture

PhD Scholarship Available at USQ Australia

Research Topic – Manufacture of Aerospace Structures using Automated Fibre Placement.

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PhD Studentship– Advanced Composite Materials Manufacturing

Department:                  Centre for Future Materials, University of Southern Queensland, Australia

 

Classification:              PhD (Higher degree by research) in the following area

 

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Full PhD scholarship available - composite material modelling at University of Southern Queensland

We are looking for a PhD candidate to work on numerical modelling of composites manufacturing. Automation drives down the production cost in composites manufacturing. The project focus on self-learning process modelling coupled with real-time pressure field monitoring. The integrated virtual-physical approach aims for : (1) real-time prediction of defect formation, and (2) real-time optimisation of process correction parameters. Self-learning process modelling is the enabling technology towards smart factory.

Remuneration

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