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CFD Jobs Database - Job Record #18039

Job Record #18039
TitleBoeing-supported PhD in Fluids/Propulsion/Machine Learning
CategoryPhD Studentship
EmployerUniversity of Melbourne
LocationAustralia, Victoria, Melbourne
InternationalNo, only national applications will be considered
Closure DateTuesday, November 01, 2022
Description:
A fully funded PhD Scholarship is available for truly outstanding candidates to work on a project at The University of Melbourne (UoM) in collaboration with Boeing Australia. This innovative Boeing aerospace project seeks PhD candidates interested in Fluid Dynamics / Propulsion / Machine Learning
The projects provide an opportunity to be part of a PhD programme at a leading research institution, working collaboratively with UoM and Boeing supervisors on research with commercial impact. This research alliance scheme pairs students with an Advisory team that includes UoM and Boeing Supervisors, as well as a generous scholarship package.
Boeing is the world's largest aerospace company and manufacturer of commercial airplanes and defence, space and security systems.  If you love modern flying and space machines and are inspired to build the next generation of air and space vehicles, you should consider joining us!
UoM is ranked #33 in the World according to 2023 QS World University Rankings, so your UoM PhD will be highly regarded wherever your career takes you. 
Imagine, your PhD work contributing towards a range of aerospace manufacturing programs, including one of most exciting aircraft projects in recent Australian aerospace history – the first military combat aircraft in 50 years to be designed, built and manufactured in Australia.

Project description

As an industry sponsored PhD program, we aim to achieve not only academic excellence, but also significant advances to real-world products, for example, to enable design of engine intakes that allow for safe and efficient operation of the propulsion system. Current numerical design tools cannot reliably predict flow behaviour in such intakes and thus time-consuming and costly testing is required. This project envisions the development of the next generation of design tools that can improve predictive accuracy and thus reduce the amount of physical testing needed, thus accelerating technology development. Novel machine learning techniques will be integrated with computational fluid dynamics in order to create novel physics-informed models to be used for intake design. 
In another project, we envision the development of an approach to augment engine thrust for a short time.
To achieve the above visions, an interdisciplinary approach will be required combining research threads in: 
•	Propulsion
•	Computational Fluid Dynamics,
•	Machine learning, and
•	Design optimization
The successful candidates will work in an industry/academic team and also individually to architect, develop and physically prototype a holistic system for industry use.

About you

This project would suit someone interested in academic research in the areas of Fluid Dynamics, CFD, Machine Learning, or other relevant fields. If you have also worked in industry, you would be highly competitive to take on this challenge.
We are seeking a PhD candidate with the following skills:
•	Demonstrated research experience in the field of Aeronautics and Astronautics, Mechanical Engineering, or Applied Mathematics.
•	Demonstrated ability to work independently and as part of the team.
•	Demonstrated time and project management skills.
•	Demonstrated ability to write research reports or other publications to a publishable standard (even if not published to date).

Eligibility

To be eligible, you must:
•	be nominated by the Advisory team and enrolling school or institute at UoM
•	be assessed by the Graduate School as meeting all conditions for admission to the higher degree by research program and be competitive for scholarship.
•	obtain security clearance from Boeing as part of the application process

We are firmly committed to the principles of Diversity and Inclusion, and all applications will be evaluated relative to prior opportunities. Workplace accommodations for people with a disability can be raised prior to the application through a direct email to the supervisors or discussed with short-listed candidates during the interview phase. Candidates from underrepresented groups are particularly encouraged to apply.

Contact Information:
Please mention the CFD Jobs Database, record #18039 when responding to this ad.
NameRichard Sandberg
Emailrichard.sandberg@unimelb.edu.au
Email ApplicationYes
Record Data:
Last Modified15:47:09, Tuesday, September 27, 2022

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