Deadline: Thursday10 May 2018
Fully Funded PhD on ‘Multimodal Physiological Markers of Affect for Automated Depression Analysis’ at The University of Sheffield, Department of Automatic Control and Systems Engineering.
A full time PhD Studentship is now available at the University of Sheffield, starting in September 2018. The studentship offers a 4-year funded PhD scholarship open to all UK and EU applicants.
The prestigious A*STAR-Sheffield scheme gives the PhD student the unique opportunity to carry out part of their studies in Singapore as part of a partnership agreement with the Agency for Science, Technology and Research (A*STAR). The prospective PhD student will receive half of their training (2 years) in Singapore.
Supervision on this project will be provided by Dr Mahnaz Arvaneh from Department of Automatic Control and Systems Engineering and co-supervision of Dr Liat Levita from School of Psychology. The project will run in partnership with the Institute for Infocomm Research (I2R) in Singapore with co-supervision of Dr Haihong Zhang.
Depression is the most common mental health disorder with high societal costs. It is the most common cause of disability worldwide and a major risk factor for suicide. Individuals with depression experience persistent and pervasive low mood and anhedonia. Despite high prevalence and social impact, there are no laboratory-based assessment tools that can objectively measure severity of depression and its recovery.
Typically, depression is assessed by clinicians using questionnaires and interviews, risking a range of subjective biases and misinterpretations. Our main ambition is to develop assistive technologies supporting clinicians in diagnosis and monitoring the progress of depression in objective and automated ways.
To this line, this project aims to identify robust and reliable multimodal physiological markers of affective states and investigate their effectiveness in detecting and monitoring depression in real-time. Thus, we will develop advanced multimodal signal processing pipelines and machine learning techniques for mapping physiological signals (i.e. brain, heart and skin electrodermal signals) recorded during passive and active mood challenging tasks onto reliable diagnostic and prognostic metrics of depression. In addition, the identified multimodal physiological markers can be potentially used for improving the treatment of the patients with depression using biofeedback methods.
A good degree (a first or upper second class UK honours degree or equivalent qualifications gained outside the UK) in engineering, mathematics, computer science, Computational Neuroscience, or subjects where signal processing and machine learning are applied
Residency restrictions – awards are open to UK and EU applicants only.
Contact Dr Mahnaz Arvaneh on email@example.com sending a CV with a cover letter detailing your interests and suitability for the role. Please mention ASTAR-Sheffield PhD in the subject of your email.
Supports Home / EU fees and a maintenance stipend at the RCUK rate.
Closing date: 10.05.2018