Research Associate, Electrical & Software Engineering

Description

Electrical & Software Engineering in the Schulich School of Engineering invites applications for a Research Associate. This Full-time Fixed Term position is for approximately 6 months (based on length of grant funding), with the possibility of extension.

The Department of Electrical and Software Engineering, in the Schulich School of Engineering, conducts research and trains highly qualified personnel across engineering fields, including machine learning, computer vision, healthcare and edge computing.

This position reports to the faculty supervisor (Principal Investigator) and works as an individual contributor within a research team; it has no direct reports. The work supports a defined, term-limited research project that develops a wearable, multi-modal edge-AI system for prehospital emergency care. It requires close collaboration with internal and external stakeholders, including clinical collaborators and an operational end‑user partner (STARS Air Ambulance).

The position works 35 hours per week and is part of Support Staff (AUPE). Work is primarily laboratory‑and computer-based; occasional travel to partner or field‑testing sites may be required.

Summary of Key Responsibilities (job functions include but are not limited to):

• Develop, adapt, and integrate the audio (speech‑to‑text, treatment and medication extraction) and video (patient tracking, body‑region parsing, injury recognition) functions into a single synchronized pipeline on a compact edge device.
• Build the timing and association layer that aligns audio and video events to a shared timeline for the same patient.
• Optimize models to run within the size, weight, and power limits of a wearable platform, and generate confidence scores for outputs.

• Design and run evaluations of the integrated system against representative prehospital scenarios, measuring the accuracy of fused outputs using standard performance metrics.
• Assess performance across patient subgroups (e.g., skin tone, body type, age, sex) to support fair and reliable results.
• Document validation protocols, findings, and limitations.

• Prepare, curate, and manage datasets for training and validation, handling sensitive and health‑related information in accordance with privacy requirements.
• Maintain reproducible experiment tracking, code, and records.

• Collaborate with the research team, clinical collaborators, and the operational end‑user partner (STARS Air Ambulance) to refine requirements and inform integration decisions.
• Contribute to technical reports, presentations, and peer‑reviewed publications, and present results to the team and project partners.

• Contribute to project planning, milestone reporting, and continuous improvement.
• Perform other related duties as assigned.

Required:

• Graduate degree (Master's or PhD) in electrical or software engineering, computer science, biomedical engineering, or a related field; an equivalent combination of education, training, and experience will be considered.
• Minimum of two years of experience either during graduate studies or in industry developing and evaluating machine learning and deep learning models, including work in computer vision and/or speech and natural language processing.
• Demonstrated experience training, optimizing, and benchmarking models using standard performance metrics (e.g., precision, recall, ROC‑AUC).
• Proficiency in Python and modern deep learning frameworks (e.g., PyTorch, Tensor Flow).
• Strong organizational, communication (verbal and written), and interpersonal skills.
• Ability to work both independently and collaboratively within a multidisciplinary team.
• Ability to document methods and results clearly for technical and non‑technical audiences.

• Experience deploying or optimizing models for resource‑constrained or edge hardware (e.g., NVIDIA Jetson) is an asset.
• Experience with multi‑modal data and sensor fusion is an asset.
• Experience working with clinical, healthcare, or other sensitive data, or in a regulated environment, is an asset.
• Knowledge…