Academic Staff Vacancies

PHD Scholarships for the ARC Training Centre for Innovation in Biomedical Imaging Technology

The Australian Research Council Training Centre for Innovation in Biomedical Imaging Technology (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. The purpose of this national centre is to provide research training in the development of ‘smart’ probes and ‘smart’ scanning techniques in conjunction with industry partners. Students will undertake industry-driven research to overcome bottlenecks in the development and application of novel diagnostics, therapeutics and theranostics and to inform changes in regulatory policy that support industry growth. Scholarships will include an industry placement for one year.

CIBIT’s research themes are:

Theme 1: ‘Smart’ Probes: Diagnostics, therapeutics and theranostics for Precision Medicine in cancer.

Theme 2: ‘Smart’ Scanning: Harnessing the digital revolution to improve diagnostic imaging cost-effectively.

Applications are invited from outstanding and enthusiastic graduates with relevant backgrounds. Students may be domestic or international of high scholarly calibre and will have a First Class Honours degree, Masters degree or equivalent.   Applicants must meet the requirements for admission into the UQ Graduate School PhD program ( and should also be eligible for a UQ Graduate School Scholarship (UQGSS) ( 

PhD Scholarship - Novel Diagnostics, Therapeutics and Theranostics

Theme 1 Project areas include:

  • New methodologies for simple and efficient radiolabelling of peptides and macromolecules.
  • New theranostics, diagnostic radiopharmaceuticals, antibody-drug conjugates for cancers including prostate and ovarian cancer, glioma, melanoma and osteosarcoma.
  • Health economics of targeted drugs and diagnostic agents.

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PhD Scholarship - Biomedical Imaging Technology

Project areas in Theme 2 include:

  • Machine learning and big data (multimodal imaging, clinical and genomic data).
  • New MRI and PET methodologies to increase diagnostic accuracy, patient comfort and safety.
  • Regulatory and reimbursement frameworks for artificial intelligence in imaging.

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