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Medical Imaging PhD Projects, Programs & Scholarships in Australia

We have 11 Medical Imaging PhD Projects, Programs & Scholarships in Australia

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Showing 1 to 11 of 11
  Understanding the mechanisms of neuroendocrine tumour response to radionuclide therapy
  Prof R Hicks, Assoc Prof C Cullinane
Applications accepted all year round

Funding Type

PhD Type

Neuroendocrine tumours (NET) represent a heterogeneous group of tumours that arise in specialized cells found throughout the body.
  Hearing colour and seeing sound - switchable polymer molecular imaging agents for visualizing disease progression
  Dr N Boase
Application Deadline: 30 September 2020

Funding Type

PhD Type

Molecular imaging aims to use non-invasive techniques to probe and visualize molecules in the body, which can be used to measure biochemical processes and disease progression.
  Enhancing Adoption of 3D Printing in Medical Technology Industry through Data Driven Decision Making
  Dr S Tavassoli, Assoc Prof B Abbasi
Applications accepted all year round

Funding Type

PhD Type

The project investigates the adoption of disruptive technologies by focusing on the case of 3D Printing (3DP) in the Medical Technology (MedTech) industry.
  Characterising and editing the age stage of somatic cells
  Dr C Nefzger
Application Deadline: 15 June 2020

Funding Type

PhD Type

On a cellular level, age appears to be a largely epigenetic phenomenon that can be manipulated by pluripotency induction. Transdifferentiation and iPS technologies are forms of transcription factor (TF)-mediated cell reprogramming.
  Targeting inflammation to prevent brain swelling following paediatric head injury
  Dr F Corrigan
Applications accepted all year round

Funding Type

PhD Type

Applications are invited to undertake a PhD/Higher Degree by Research (HDR) to investigate how the inflammatory response to a traumatic brain injury differs in the paediatric population compared to adults.
  Engineering Processable, Tough Hydrogels with Biological Activity
  Prof F Deghani
Application Deadline: 20 July 2020

Funding Type

PhD Type

This project will aim to design a type of hydrogel with broad applications in soft robotic, medical devices such as heart valves, vasculature, and development of in vitro model that mimic physical properties of digestion system including colon, intestine and stomach.
  Data Analytics and Optimisation to Improve Decision Making
  Dr S Chakraborty
Applications accepted all year round

Funding Type

PhD Type

This project area aims to utilise and develop traditional and advanced data analytics models such as Fuzzy sets, Multiple Criteria Decision models, Agent based modelling, Bayesian modelling, deep learning models etc.
  Non coding RNAs in Adrenocortical Cancer
  Prof G. Hutvagner
Applications accepted all year round

Funding Type

PhD Type

Background. Industry, society or clinical need for project and scientific rationale. Adrenocortical carcinoma (ACC) is a rare disease which occurs with an annual incidence of 2-4 cases per million with a five-year survival of less than 16-38%.
  Coronary artery atlas using deep learning for early risk prediction of heart attacks
  Dr S Beier
Applications accepted all year round

Funding Type

PhD Type

This project will work with the largest dataset of medical images for coronary arteries surrounding the heart in more than 1200 patients.
  Sudden Coronary Artery Dissection Modelling for Medial Image detection and classification markers
  Dr S Beier
Applications accepted all year round

Funding Type

PhD Type

Sudden Coronary Artery Dissection or SACD often causes sudden death in young females below 44 years. This non-atherosclerotic condition is caused through the split of the internal arterial wall, which narrows the effective arterial cross-section and prevents sufficient blood supply causing heart attacks.
  Towards personalised coronary artery disease consideration using medical image processing and computational modelling
  Dr S Beier
Applications accepted all year round

Funding Type

PhD Type

This project is highly interdisciplinary, and will involve working with (and extending) a suite of existing software and computational models based on clinical imaging data.
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