Public Health Ontario x Vector Institute

Project Focus:
Lyme disease is a reportable disease transmitted in North America via blacklegged ticks.
Prevention of Lyme disease after tick bite requires prompt prophylaxis to be administered
shortly after the tick has been removed. Prophylaxis decisions are guided by 1) whether the tick
is a blacklegged tick, and 2) whether Borrelia burgdorferi is known to be prevalent in the ticks in
the region where the bite occurred. The main goal of this project is to develop state of the art
deep learning and computer vision algorithms for automatic identification of blacklegged ticks
from the tick images. Images collected with the Public Health Ontario’s (PHO’s) app will be used
in this project.

Location: PHO Laboratory, MaRS Discovery District, Toronto, ON.

Internship Duration: 4-6 months; full time (37.5 hours per week).

Remuneration: ~ $5,000 per month

About the Organization:
PHO is a provincial crown agency that provides scientific and technical advice and support to
those working across sectors to protect and improve the health of Ontarians. Our organization
carries out and supports activities such as population health assessment, public health
research, surveillance, epidemiology, planning and evaluation. We also operate the provincial
public health laboratory service, undertaking important tests for clinicians in primary care and
hospitals as well as for public health units. PHO’s objectives are outlined in the Ontario Agency
for Health Protection and Promotion Act, 2007.

This project is performed in partnership with the Vector Institute, a not-for-profit organization
with a vision to drive excellence and leadership in Canada’s knowledge, creation and use of
artificial intelligence (AI) to foster economic growth and improve lives. Vector works with
Canadian industry and public institutions to ensure that they have the people, skills and
resources to be best in class at the use of AI. By working with partners in the health and
academic sectors, Vector will support and enable AI research and innovation across a wide
range of health topics.

Purpose of the Position:
To provide technical and computational support for research and clinical operations with a
focus on software development and management, quality control, analysis, transformation, visualization, and presentation of data. The clinical and research projects include a mix of short-
term support tasks and long-term goals. Support tasks and methods may eventually transition into standardized workflows.

Role and Responsibilities:
• Provides support in public health data management in creating and maintaining
databases, data warehouses and data marts.
• Develop computer vision and deep learning prototypes by preprocessing images,
implementing models, and creating demos.
• Conduct performance evaluation and optimization of algorithms.
• Deploy deep learning models in a mobile app.
Qualifications and Education Requirements:
• Pursuing or is a recent graduate from a post-secondary program in Computer Science,
Computer Engineering, or a related field.
• Strong background in machine learning, deep learning, and algorithm design.
• Deep Learning model training with GPUs and inference on mobile devices.
• Experience with parameter and architecture tuning of deep learning algorithms.
• Knowledge of object detection and segmentations as well as recent case studies
(examples of pre-trained models).
• Familiar with foundation of convolutional neural network, deep convolutional models,
and transfer learning in computer vision.
• Proficiency in Python programming and open source libraries like PyTorch, TensorFlow,
or Keras.

Application Requirements:
• Cover Letter
• Résumé

Please submit all application material by August 14, 2019 to Vanessa Allen at and copy with “Public Health Ontario AI
Internship” in the subject line.

Applications should not exceed two pages in length.

Tagged as: Bioinformatics, Computer Vision, Deep Learning, Health

Time Commitment :

4 to 6 months, 37.5 hours per week

Contact Person (Name):

Vanessa Allen

Contact Person (Email):
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