CIBIT PhD Top Up Scholarship in molecular imaging - artificial intelligence based/parametric methods

Summary

Enrolment status New students, Currently enrolled students
Student type Domestic students, International students
Level of study Higher Degree by Research
Study area Engineering and Computing, Health and Behavioural Sciences, Science and Mathematics
HDR funding type Top-up Scholarship
Scholarship value Up to $5,800 per annum, tax free
Scholarship duration Three years with the possibility of two 6-month extensions in approved circumstances
Opening date 2 October 2019
Closing date 27 October 2019

Scholarship description

The Australian Research Council Training Centre for Innovation in Biomedical Imaging Technology (CIBIT) is seeking high-achieving graduate students to undertake postgraduate research into the development and application of novel molecular imaging methodologies. We have exciting opportunities to work with world leading academic researchers and industrial partners focused on the development and translation of new molecular imaging methodologies to the clinic. In particular, the candidate will be involved in development of quantitative methods to improve the sensitivity and specificity of PET and MRI-PET imaging.

Projects are available in the following areas:

  1. Application of artificial intelligence and machine learning methods
  • Train a neural network to localize epilepsy foci and assist surgical planning.
  • Develop a generative adversarial network (GAN) to create a CT scan out of a PET, or vice versa.
  • Tumor/organ segmentation directly from sinogram.
  1. Application of parametric methods
  • Validation of Parametric PET Population based input function
  • Validate Patlak with specific antibody tracers
  • Develop a GAN to predict distribution volume images from SUV and CT images.

Applicants should have a background in the physical sciences including disciplines such as biomedical engineering, engineering, information technology, physics or a related field. Applicants should possess analytical and computational skills, including working with equations, simulations and computer programs and an interest in Imaging Modalities and Healthcare IT Solutions.

Successful students will have the opportunity to undertake internships with our industry partners.

CIBIT is a multidisciplinary collaboration between researchers at The University of Queensland’s Centre for Advanced Imaging and partners in the Medical Technologies and Pharmaceutical industry. CIBIT’s research encompasses two major themes; this project will be specifically related to Theme 2: Harnessing the digital revolution to improve diagnostic imaging cost-effectively.

The Centre for Advanced Imaging

The Centre for Advanced Imaging (CAI), a strategic initiative of The University of Queensland, is a leading imaging research facility in Australia, and one of a handful in the world. It brings together the skills of a critical mass of researchers and state-of-the-art, world- or Australian-first imaging research instruments including NMR, EPR, MRI, PET, CT, optical imaging and an on-site cyclotron and radiochemistry facilities. CAI hosts the largest Node of the National Imaging Facility (NIF).

CAI conducts research across the spectrum from development of new imaging technologies, analysis of molecular structure, synthesis of MRI and PET biomarkers targeting fundamental biological processes to studies of major diseases affecting a range of organ systems, through to imaging economically significant agricultural animals and plant material, minerals and construction materials.

A multidisciplinary, cohesive student community have come together from all over the globe to CAI to undertake research training.  The Centre has an active student association (STAC) that provides many opportunities for networking and professional development, a supportive mentoring structure that will enhance personal and professional growth, an annual symposium and a well-attended weekly seminar program which attracts high profile National and International speakers.

Further details on the Centre for Advanced Imaging and ongoing research can be found on CAI’s website.

CAI is committed to supporting the career growth of female researchers and have a number of initiatives to support females in developing and achieving a fulfilling research career at the institute. For more information, please visit our CAI Women in Imaging website.

Eligibility

To be eligible, you must meet the entry requirements for a higher degree by research.

Applicants must be in receipt of or apply for and be awarded a living allowance scholarship of at least the Research Training Program rate ($28,092 for 2020, indexed annually) to be eligible to receive this top-up.

Applications are closed.

Before you apply

If this scholarship has rules, download and read them before applying.

How to apply

To be considered for this scholarship, please email the following documents to Professor David Reutens (administrator@cibit.org.au

  • Cover letter
  • CV
  • Academic transcript/s
  • Proof of meeting English Language requirements

Please note the following: Submitting the above documents does not constitute a full application for admission into The University of Queensland's PhD program. If you are selected as the preferred applicant, you will then be invited to submit a full application for admission. You can familiarise yourself with the documents required for this process on the Graduate School's website.

Selection criteria

Applicants should have a background in the physical sciences including disciplines such as biomedical engineering, engineering, information technology, physics or a related field. Applicants should possess analytical and computational skills, including working with equations, simulations and computer programs and an interest in Imaging Modalities and Healthcare IT Solutions.

Applications are closed.

Contact

Professor David Reutens
Applications are closed.

Terms and conditions

Read the policy on UQ Research Scholarships.

A domestic part-time student with carer’s responsibilities, a medical condition or a disability, which prevents them from studying full time may be eligible for scholarship consideration, on a case by case basis.

Applications are closed.