PhD Scholarship, Seizure Detection

Location
Hawthorn, Australia
Posted
08 Mar 2019
End of advertisement period
01 May 2019
Ref
0541_03/19_RTR
Contract Type
Fixed Term
Hours
Full Time
  • Telecommunication Electrical Robotics Biomedical Engineering
  • Seizure Detection
  • SUPRA scholarship $27,082/year, 3 year

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:

  • Bachelor (Honours) or Master degree (or equivalent) in Biomedical Engineering, Electrical and Electronic Engineering, Mathematics, Physics or similar discipline.
  • Passionate and have interest in pursuing PhD degree.
  • Able to conduct challenging research independently.
  • A team player who has good interpersonal skills and can collaborate well with others.

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 tkameneva@swin.edu.au.

If you are experiencing technical difficulties with your application, please contact the Recruitment team on staffrecruitment@swin.edu.au

Applications close 5 pm on 3 May 2019, unless filled prior