PhD Research Scholarship in Materials Engineering
- Full Time
Five PhD Research Scholarships in Materials Engineering
School of Mechanical and Mining Engineering
The University of Queensland's School of Mechanical and Mining Engineering maintains a world-class reputation and is at the cutting-edge of engineering education, research and expert consultation across the fields of mechanical, materials, mining, aerospace and mechatronic engineering. The School’s reputation for research excellence attracts academia from around the world. Our diverse research programs address the evolving needs of industry and society, and contribute to economic and social development. The School’s established research strengths in hypersonics, light metals, geothermal energy and mining technology, are complemented by continued growth in areas such as solar thermal, composites, steels, surface engineering, metals manufacturing and rock mechanics.
Within the School, Prof Mingxing Zhang (http://researchers.uq.edu.au/researcher/404) leads a research group who work on grain refinement of cast metals, metal additive manufacturing, surface engineering, alloy development, steels and wear-resistant alloys for the mining and mineral industries. The group has five scholarships available to applicants who hold with an Honours or equivalent qualification in Materials Engineering, mechanical engineering or related discipline, to commence a Doctor of Philosophy (PhD) in the areas of materials science and engineering
Two PhD projects will be conducted within the framework of the ARC Industrial Transformation Training Centre in Alloy Innovation for Mining Efficiency (mineAlloy, http://minealloy.com.au/), which is a collaboration between, Deakin University, The University of Queensland and Monash University.
The topics are:
- Control of carbide morphology in high chromium cast irons.
- Control of carbide morphology in cast high manganese steels.
- Grain refinement of high carbon high manganese steels
The successful candidates will receive training in the physical metallurgy of metals, crystallography of phase transformations, grain refinement of cast metals, science of wear, and in the required laboratory measurement techniques. The successful candidates will also have opportunities to work in an Australia industry company for 12 months as part of their PhD training process to gain exposure to both supplier (manufacturing) and end-user (mining) industries.
Another PhD project will be conducted within the School working on an industry-funded project on low carbon bainitic steel.
Candidate can select one of the following as his/her PhD topic:
- Development of new generation of low carbon bainitic steels and the associated processes for structural applications.
- Grain refinement of low carbon bainitic steels.
- Microstructure formed in low carbon alloy steels.
The successful candidates will receive training in the physical metallurgy of metals, crystallography of phase transformations, grain refinement of cast metals, forming of steel, and in the required laboratory measurement techniques. The successful candidates will have an opportunity to visit and/or work in one of the world biggest steel making companies to gain industrial working experience.
Another two PhD projects will be conducted within the School of Mechanical and Mining Engineering and School of Information Technology and Electrical Engineering working on an Australian Research Council (ARC) Discovery Project to develop new generation of aluminium alloys through big data analytics.
The topics are:
- Development of a big data analytic knowledge model that is capable of correlating the chemical compositions and processes of aluminium alloys to the mechanical properties.
- Design and development of new aluminium alloys and the associated processing using the big data analytic model.
The successful candidates will receive training in the physical metallurgy of metals, manufacturing of aluminium alloys, fundamental of big data analytics and machine learning process, and in the required laboratory measurement techniques.
Domestic candidates should have an Honours degree or equivalent in Materials Engineering, mechanical engineering (or related disciplines). Candidate to work on the big data analytics should have an Honours degree or equivalent in Information Technology. All cadidates must meet the requirements of admission into the PhD program (https://graduate-school.uq.edu.au/uq-research-degrees). Applicants should also be eligible for an Australian government-funded or UQ-founded Scholarship or equivalent (https://graduate-school.uq.edu.au/scholarships).
International candidates will be considered if they have attained a Masters degree (by course work or by research) and have published at least one journal paper. They are expected to apply for an International Postgraduate Research Scholarship (IPRS) and other UQ scholarships (https://graduate-school.uq.edu.au/scholarships) or external scholarship. International applicants must meet the University of Queensland's English Language Proficiency (ELP) requirements (https://graduate-school.uq.edu.au/english-language-proficiency-requirements).
For all the projects to be applied for, the student will be required to have and/or develop the following knowledge, skills and attributes:
- Sound knowledge of the science and engineering of metals.
- Capacity to participate in production of experimental iron, steel and aluminium castings in the University’s experimental foundry, taking part in quality assurance and traceability monitoring.
- Well-developed laboratory and practical skills, including safe operation of rotating equipment, accurate measurements and data recording.
- Capacity to participate in field trials at remote mine sites, liaising with site personnel and taking detailed on-site measurements of worn multi-specimen test plates.
- Ability to operate TEM, SEM and EDS instruments.
- Demonstrable commitment to good practice in data management.
- Ability to communicate technical concepts and the logical “story” of the project in clearly written English.
- Sound knowledge of big data analytics.
Previous research and/or industry experience in fields such as foundry technology or minerals industry maintenance or big data analytics would be viewed favourably.
Candidates will also need to be aware of and comply with legislation and University policy relevant to the duties undertaken, including:
- the University’s Code of Conduct ;
- requirements of the Queensland occupational health and safety (OH&S) legislation and related OH&S responsibilities and procedures developed by the University or Institute/School ;
- the adoption sustainable work practices and compliance with associated legislation and related University sustainability responsibilities and procedures ;
- requirements of the Education Services for Overseas Students Act 2000, the National Code 2007 and associated legislation, and
The base stipend will be at the rate of AUD $27,082 per annum (2018 rate) tax-free for three years with the possibility of two six month extensions in approved circumstances. For the candidates who have been successful in obtaining any government-funded scholarship or UQ-funded scholarship or any other external scholarship, a top-up scholarship of $6000 to $10000 per annum tax-free may be awarded.
For specific questions about the role please email Professor Mingxing Zhang, Mingxing.Zhang@uq.edu.au.
To submit an application for this role, use the Apply button below. All applicants must supply the following documents:
- Covering letter which demonstrates that you meet the requirements for the PhD program and addressing the desired skills and attributes;
- Curriculum vitae detailing education, professional experience, research experience, publications, and relevant competencies;
- Complete academic record (including GPA scores/grades and grading scale details);
- Names and contact details of three referees.
Application closing date
Domestic applicants: Tuesday, 31 July 2018 (11:55 pm Eastern Australia Standard Time)
International applicants: Friday, 29 June 2018 (11:55 pm Eastern Australia Standard Time)