Research Assistant

Description:

Full Job Description

The position is funded under an Australian National Health and Medical research Council Grant: “Automated methods for evaluating structural vascular disease” and Department of Health WA Near-miss grant, “A novel machine-learning approach to reduce falls in older community-dwelling Australians”. Successful candidate/s will contribute to the application of existing Deep Learning methods to problems in Medical and Health related image analysis. Particularly, the key focus will be to apply machine learning methods to extract calcification measures off images of the abdominal aorta and correlate these measures with clinical data as well as to develop methods to predict falls in the elderly using whole body DXA scans.

The Role

The School is looking for enthusiastic Research Assistants who possess knowledge and expertise in applications in the following areas:

Machine Learning

Deep Learning

Computer Vision

Medical Image Processing

What you will do

The successful candidate/s are expected to run deep learning networks already developed by our group to test unlabelled 2D images. The candidate/s will be trained on the specifics of these networks.

Skills & Experience

You should possess the skills of independently running deep learning models in Pytorch. You must have an undergraduate degree in an appropriate field (e.g., Computer Science, Mathematics, Electrical Engineering). Strong mathematical ability will be an advantage.

The successful applicant will also demonstrate personal attributes that are congruent with the University’s values of Integrity, Respect, Rational Inquiry and Personal Excellence.