PhD scholarship: Computational Magnetic Resonance Imaging
Apply now Job No:511430
Area:Faculty Of Engineering, Architecture & Info Tech
Salary (FTE):RTP Scholarship NON-BANDED ($28,092.00 – $28,092.00)
Work type:Full Time – Scholarship
Location: St Lucia
UQ ranks in the top 50 as measured by the Performance Ranking of Scientific Papers for World Universities. The University also ranks 46 in the QS World University Rankings, 42 in the US News Best Global Universities Rankings, 66 in the Times Higher Education World University Rankings and 54 in the Academic Ranking of World Universities.
UQ offers a highly collaborative environment. In 2015, UQ scored at world standard in >90% of fields assessed and this is further complemented by the great intellectual environment within UQ and unique infrastructure. We have access to one of only 2 human Ultra-high field MRI research scanners in Australia, a high performance distributed data storage system (UQRDM) and high performance computing resources including a dedicated GPU cluster (WIENER).
Supervisor – Dr Steffen Bollmann
Detailed understanding of the human brain requires the integration of information from different spatial scales and modalities. Major research initiatives, such as the ten-year Human Brain Project and the BRAIN Initiative, have identified the need to merge information across all levels of brain organisation to arrive at a comprehensive and coherent model of the brain. Although there has been substantial progress in understanding the individual levels of brain organisation, it is still unclear how we can link these different scales to obtain a thorough understanding of brain structure and function. Bridging the gap between different levels of brain organisation, such as linking brain microstructure on a cellular level observable using ex vivo histology to macroscopic properties observable using in vivo magnetic resonance imaging requires advanced methodologies, large datasets, and developing new computational frameworks.
This PhD project aims to build computational models for characterising tissue properties from multi-modal magnetic resonance imaging data. The candidate will enrol through the School of Information Technology and Electrical Engineering.
A working knowledge of medical imaging analysis would be of benefit to someone working on this project.
To be eligible to apply, you must also meet the entry requirements for Higher Degrees by Research at UQ. Applications will be judged on a competitive basis taking into account the applicant’s previous academic record, publication record, honours and awards, and employment history.
The applicant will demonstrate academic achievement in the field/s of medical imaging and the potential for scholastic success.
A background or knowledge of software engineering, high performance computing and statistics is highly desirable.
The 2021 Research Training Program (RTP) living allowance stipend rate is AUD$28,597 per annum (indexed annually), which is tax-free for three years with two possible extensions of up to 6 months each in approved circumstances (conditions apply ).
For further information, please contact Dr Steffen Bollmann at email@example.com .
How to apply
To apply for admission and scholarship, follow this link . There is no separate application for scholarship because you will have the opportunity to request scholarship consideration on the application for admission.
Before submitting an application you should:
When you apply, please ensure that under the scholarships and collaborative study section you:
- Select ‘My higher degree is not collaborative’
- Select ‘I am applying for, or have been awarded a scholarship or sponsorship’.
- Select ‘Other’, then ‘Research Project Scholarship’ and type in ‘BOLLMANN IMAGING’ in the ‘Name of scholarship’ field.
See an example of what you have to do
Learn more about applying for a higher degree by research at UQ
Advertised:28 Sep 2020
Applications close:11 Oct 2020 (11:00 PM) E. Australia Standard Time
click the link to apply http://search.jobs.uq.edu.au/caw/en/job/511430/phd-scholarship-computational-magnetic-resonance-imaging