Posting Date: Nov 1st, 2017
Job Title: Post-Doctoral Fellow, School of Computing, Queen’s University
Funded by: IBM and CIMVHR (Canadian Institute of Military and Veteran Health Research)
Department: School of Computing, Queen’s University
Collaborators: University of Manitoba, Western University
Project Title: Defining Post Traumatic Stress Disorder (PTSD) in Primary Care Electronic Medical Record (EMR) Data to Explore Prevalence, Patient Characteristics and
Primary Care Experiences of Veterans, Families of Military Service Members and the General Population
Research Areas: Natural Language Processing (NLP) for medical text, text mining, machine learning, knowledge representation and management and diagnosis of PTSD
Supervision: Dr. Farhana Zulkernine and academic collaborators (primarily Dr. Alexander Singer, University of Manitoba)
Remuneration: $45,000 CDN per year (with deductions for benefits)
Start Date: Feb 1st, 2018
Duration: 2 years
Application Deadline: Until hired
Application Procedure: Apply by email by sending the application package to
Qualifications: We seek a candidate with proven expertise in text data mining, natural languageprocessing (NLP) preferably for medical data, deep learning/machine learning, knowledge management using big data storage systems and knowledge sharing using cloud services. The work will focus on using NLP and text mining techniques to extract terms that are representative of PTSD diagnosis from doctors’ chart notes stored in the EMR systems. The data must be anonymized and analyzed to extract indicators of possible development or progression towards developing PTSD and diagnosis of PTSD in different sectors such as veteran population, their families and general public to understand and evaluate quality of primary care for patients with PTSD. The extracted and analyzed data must be stored to enable knowledge translation and sharing with the research community.
A PhD in Computer Science or a comparable qualification is required. The successful candidate must have a good research record with publications in relevant international conferences and journals in medical data analytics, text analytics, and machine learning. The candidate is expected to have an active role in various collaboration efforts with other universities and industry, both at the national and international level. Good communication and project management skills and willingness to work in a team are essential. Additional information about Dr. Zulkernine’s research and the project can be found at http://cs.queensu.ca/~farhana/. The position is for two years and is funded by IBM and CIMVHR (Canadian Institute for Military and Veteran Health Research). The candidate will be working in the Big Data Analytics and Management laboratory (BAM Lab) at School of Computing, Queen’s University but closely collaborating with Dr. Alex Singer at the University of Manitoba and other collaborators involved in this project. Periodic reports must be presented about the status and progression of the work.
The School of Computing is one of the premier computing departments in Canada with about 30 faculty members and close to 150 graduate students. Kingston is Canada’s first capital and is centrally located between Toronto, Ottawa, and Montreal. The United Nations rates Canada as one of the best countries in the world to live. The project will provide a unique opportunity to the candidate to work with multiple experts on an interdisciplinary research which will contribute to Canada’s health care system and allow exploration of cutting edge tools and techniques in text data analytics, knowledge management, and machine learning.
Please send an application package to firstname.lastname@example.org containing:
- A cover letter explaining experiences relevant to the project.
- A curriculum vitae with detailed information regarding your academic degree,
research projects and publications.
- Names and email addresses of three referees.
- Sample publications (1-2 recent publications).
***The applicant should be a Canadian citizen/immigrant/permanent resident.
EMPLOYMENT EQUITY: The University invites applications from all qualified candidates. 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 persons of any sexual orientation or gender identity.