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Coronary artery atlas using deep learning for early risk prediction of heart attacks

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

This project will work with the largest dataset of medical images for coronary arteries surrounding the heart in more than 1200 patients.

Responsibilities evolve around the development of suitable machine learning algorithms to identify early risk markers in medical images. This will help to implement new preventative mechanisms in clinical practice aimed to safe patient lives.

Strong problem-solving skills are required and the interest in and willingness to learn about cardiovascular physiology is important.

This position is suitable for students from computer sciences or engineering and a strong interest in machine learning. Previous research experience is highly desired.

UNSW is the top engineering University in Australia, and top 35th in the world. It is reknown for being progressive and inclusive, highly valuing diverse student and staff cohort.

Female top-up scholarships are available for $10,000 per annum.
Additional internal tutor opportunities to top up your living allowance are available all year around at $56 per hour.

To find out more about the group see http://www.svmgroup.org .

Funding Notes

Applicants must have:
- a GPA higher than 3.8/4.0, or WAM >90 (for international students), or WAM >75 for Australian residents, and
- must come from a top 200 University for their under-graduate study (QS, Times or shanghai), or have more than 2 years relevant industry research experience
- meet the UNSW English language requirements View Website, and
- intend on enrolling as a full-time HDR candidate in the commencing semester after successful application.

Students with a home country sponsored scholarship are welcome.

References

Please apply with your CV, references and cover letter addressing all the selecting criteria.

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