Postdoctoral Fellowship, ARiEAL Research Centre, McMaster University
Job ID: 29553
POSITION DESCRIPTION
Bargaining Unit: CUPE Local 3906, Unit 3
Primary Location: McMaster University
Duration: 12 months with possibility of renewal
Department: ARiEAL Research Centre
Hours per week: 35
Compensation: $40,000-$60,000/year depending on experience/qualifications plus benefits in accordance with the collective agreement
Co-Supervisors:
- Dr. John Connolly, Director, ARiEAL Research Centre, Professor & Senator William McMaster Chair in the Cognitive Neuroscience of Language
- Dr. Jim Reilly, Professor Emeritus, Department of Electrical and Computer Engineering, McMaster University
JOB DESCRIPTION
Drs. John Connolly and Jim Reilly are accepting applications for a one-year (renewable) postdoctoral fellowship at the Language, Memory and Brain (LMB) Lab co-directed by Dr. John Connolly. The LMB Lab is located at the ARiEAL Research Centre, McMaster University in Hamilton, Ontario. The ARiEAL Research Centre, directed by Dr. Connolly, is an interdisciplinary research site that brings together researchers versed in experimental and applied methods, machine learning and AI, and behavioural and neurophysiological approaches to a broad range of cognitive function in both theoretical and applied settings.
Dr. Connolly’s areas of investigation include the study of cognitive functioning in brain injured populations with particular emphasis on coma, other disorders of consciousness (DOC), locked-in syndrome, and concussion. In particular, he employs EEG and related measures (e.g. event-related potentials [ERP]) to study brain injury across the age span from pediatric populations to the elderly. The postdoctoral fellow position will be in the coma research project that focuses on developing brain recording tools to enable the assessment of cognitive functioning that will also have relevance to patients who have emerged from coma and are unable to communicate through language or gesture effectively. The postdoctoral fellow will work primarily within a new CIHR/NSERC funded CHRP project on the development of a point of care system for automated coma assessment and prognosis using neurophysiological recordings and machine learning techniques. The postdoctoral fellow is also encouraged to lead his/her own research within this coma project context.
Dr. Reilly’s area of interest is in signal processing and machine learning, with specific focus on applications in neuroscience and psychiatry. He has worked extensively on the prediction of response to treatment for major depression using the EEG, in conjunction with machine learning methods. Dr. Reilly and his team in collaboration with the Connolly lab have developed new machine learning methods for analysis of ERP responses to predict emergence from coma. He is also interested in estimation of networks of the brain and the use of network parameters as features for machine learning algorithms. Dr. Reilly is a co-principal-investigator on Dr. Connolly’s CHRP project and will play a supervisory role in the machine learning aspects relating to this research.
The primary responsibility of the postdoctoral fellow is to develop and evaluate experimental paradigms appropriate for electrophysiological studies of coma/DOC populations, and work with a team developing machine learning tools and their implementation using AI approaches. The applicant is expected to aid and lead EEG testing in the hospital setting and will be in direct contact with a full team of trainees and primary care providers and research team members at the hospital. Note: ARiEAL is a research centre exclusively and this fellowship will be entirely research focused with no teaching requirements.
QUALIFICATIONS
Eligible applicants will have completed a PhD with training in cognitive neuroscience and/or biomedical engineering or related areas (e.g., Psychology, Kinesiology). Extensive experience in recording and analyzing human electrophysiological activity is essential as is experience in using and developing software for machine learning applications. Experience with interpretable machine learning, large clinical research studies, code-versioning, TensorFlow (or other frameworks), and software development will be an asset. Successful candidates will be intellectually curious, have impeccable planning skills, work well in a clinical setting, and be a highly motivated individual who can work well in teams and contribute to the translational goals of this research. A thorough understanding and experience in proper machine learning validation procedures, real-time processing of biosignals, experimental design, and statistics will be critical for a successful candidate.
HOW TO APPLY
To apply, applicants should submit a current CV, graduate transcripts, and a letter of interest (also indicating the earliest possible start date) as one attachment in .pdf format through MOSAIC or Working@McMaster portal (https://hr.mcmaster.ca/careers/current-opportunities/)). Please also arranged two letters of reference to be e-mailed directly to Ms. Chia-Yu Lin (ariealrc@mcmaster.ca) with “PDF Application – Coma & AI” and the applicant's full name in the subject line. All documentation submitted in support of your application becomes the property of McMaster University and will not
be returned.
EMPLOYMENT EQUITY STATEMENT
McMaster University is located on the traditional territories of the Haudenosaunee and Mississauga Nations and, within the lands protected by the “Dish with One Spoon” wampum agreement. In keeping with its Statement on Building an Inclusive Community with a Shared Purpose, McMaster University strives to embody the values of respect, collaboration and diversity, and has a strong commitment to employment equity. The diversity of our workforce is at the core of our innovation and creativity and strengthens our research and teaching excellence. The University seeks qualified candidates who share our commitment to equity, diversity and inclusion. While all qualified candidates are invited to apply, we particularly welcome applications from women, persons with disabilities, First Nations, Métis and Inuit peoples, members of visible minorities, and LGBTQ+ persons. Job applicants requiring accommodation to participate in the hiring process should contact the Human Resources Service Centre at 905-525-9140 ext. 222-HR (22247) or the Faculty of Health Sciences Human Resources office at ext. 22207 to communicate accommodation needs.