Queen's University, Electrical & Computer Engineering

Position ID:Queens-Electrical & Computer Engineering-POSTDOC [#13948]
Position Title: Postdoctoral Fellow in Machine Learning for Modeling Alzheimer's Disease Development
Position Type:Postdoctoral
Position Location:Kingston, Ontario K7L 3N6, Canada [map]
Subject Areas: Machine Learning / Data Science
Health Sciences
Mental Health
Artificial Intelligence
Appl Deadline: finished (2019/06/26, finished 2019/12/05)
Position Description:    

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Postdoctoral Fellow in Machine Learning for Modeling Alzheimer's Disease Development

We seek a post-doctoral research fellow (PDF) to work on building machine learned models of Alzheimer’s disease (AD) onset and progression, using a large repository of healthcare data (www.ices.on.ca). The PDF will work collaboratively with an interdisciplinary team comprising five faculty members (Drs. Geoff Chan, Dallas Seitz, Gunnar Blohm, Colleen Maxwell, and Xiaodan Zhu) and other research staff on the project. The team has expertise in machine learning and deep learning, geriatric mental health, pharmacology, and neural science. For the project, some baseline models have been built. The position provides excellent opportunities for the PDF to gain experience, hone expertise, and contribute to unleashing the power of machine learning on healthcare data in order to treat and prevent a disease with increasing socioeconomic consequences.

Start Date and Duration of Appointment

September 1, 2019 – August 31, 2020 (possibility of renewal for one additional year). There’s some flexibility with the start date, though earlier start is preferred.


Consideration will be given to applicants who (will) have completed their Ph.D. degrees by the start date, with machine learning (ML) as a key component of his/her recent research. Experience in building machine learned models using datasets in health and biological disciplines, such as medicine, pharmacology, epidemiology, and genomics, would be an asset. Applicants with experience in other ML application domains are welcome. An important consideration is the candidate’s ability to adapt quickly to working with ICES data and the team to achieve project goals.


Competitive salary (plus benefits) commensurate with qualification and exceeding the salary provision in Queen’s PDF union collective agreement.


The PDF will be mentored by faculty members on the team. Administratively, the PDF reports to Dr. Geoffrey Chan, Department of Electrical & Computer Engineering, and Dr. Dallas Seitz, Division of Geriatric Psychiatry, Department of Psychiatry.

Application Procedure

Interested applicants please email a copy of your current CV, academic transcripts, and the names and contact information of three professional references to Dr. Geoffrey Chan (chan@queensu.ca). Questions related to this position should also be directed to Dr. Chan by email. Applications will be considered until the position has been filled. 



EMPLOYMENT EQUITY: The University invites applications from all qualified individuals. Queen's is committed to employment equity and diversity in the workplace and welcomes applications from women, visible minorities, Aboriginal peoples, persons with disabilities, and LGBTQ persons.

ACCOMMODATION IN THE WORKPLACE: The University has policies in place to support its employees with disabilities, including an Accommodation in the Workplace Policy and a policy on the provision of job accommodations that take into account an employee's accessibility needs due to disability. The University will provide support in its recruitment processes to applicants with disabilities, including accommodation that takes into account an applicant's accessibility needs. If you require accommodation during the interview process, please contact Geoff Chan at chan@queensu,ca, 613-533-2939.

This employer is not accepting applications for this position through AcademicJobsOnline.Org. Please see the job description above on how to apply.
Contact: Wai-Yip Geoffrey Chan, 613-533-2939
Email: email
Postal Mail:
Electrical & Computer Engineering Department
Queen's University, Walter Light Hall
19 Union Street
Kingston, Ontario, K7L 3N6
Web Page: www.ece.queensu.ca

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