|Location:||Hawthorn - Australia|
|Funding for:||UK Students, EU Students, International Students|
£14,640.53 converted salary* per year.
|Placed On:||13th March 2019|
|Closes:||3rd May 2019|
About the project
Epilepsy is a neurological disorder characterized by recurrent seizures that are transient symptoms of synchronous neuronal activity in the brain. Epilepsy is commonly diagnosed using electroencephalography, which captures abnormal brain activity. Epilepsy affects more than 50 million people worldwide. In the United States, it is estimated that 7 out of 1,000 people live with epilepsy, in Australia, over 225,000 people live with epilepsy and approximately 3% of Australians will experience epilepsy at some point in their lives.
Electroencephalography (EEG) is often used to predict the commencement of a seizure, with varying success between participants. There is an increasing interest to use non-EEG body signals, including electrocardiogram (ECG) to help with seizures detection and prediction.
The aim of this project is to use advanced signal processing and machine learning techniques to detect and predict seizures from EEG and ECG data recorded by Seer. The project scope also includes comparing features leaned by a machine learning algorithm that are distinct between three groups of people: epilepsy patients, people with syncope syndrome and healthy controls.
The project is a collaboration between Swinburne University of Technology and Seer. The student scholarship is funded by Swinburne University of Technology, and data is provided by Seer. The student should have strong analytical skills and be interested working in a multidisciplinary environment.
Skills and experience
To be successful in this role you will need to demonstrate the following:
A full list of the selection criteria is available within the position description
Further information, contacts and support
To start an application click on 'begin' at the bottom of this page and submit a resume, cover letter and response to the Key Selection Criteria, as listed in the Position Description below.
Please do not email or send paper applications, all applications must be submitted online.
For further information about the position, please contact Tatiana Kameneva (VC STEM Fellow, Senior Lecturer) via
If you are experiencing technical difficulties with your application, please contact the Recruitment team on firstname.lastname@example.org
Should you require further support for an interview due to special needs or consideration, please contact our Diversity Consultant, Dr. Walter Robles, on email@example.com. For support or queries related to Aboriginal and Torres Strait Islander employment, please contact DeadlyCareers@swin.edu.au.
Applications close 5pm on 3 May 2019, unless filled prior
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