Goal: Develop an automated NLP system to code and analyze eConsult questions and specialist responses

Focus: Natural language processing and machine learning to automate eConsult coding and analysis 

Scope: This project develops an automated coding system for electronic consultations (eConsults), a secure online platform that allows primary care providers to send clinical questions to specialists and receive responses within days. Hundreds of consultations have been manually coded and categorized using standardized taxonomy to understand their patterns and content.

The intern will apply natural language processing and machine learning to build an automated coding system that identifies commonly asked questions and recommendations specific to disease conditions. Work will analyze eConsult cases from the Champlain eConsult BASE™ service within Eastern Ontario, including queries from family physicians expressed with clinical terms and specialists’ responses, with individual cases ranging from 100 to 500 words.

The output will enable efficient analysis of consultation patterns and improve understanding of primary care clinical needs and specialist recommendations across disease conditions. 

Desired Candidate: Graduate student (no early career professionals or undergraduates) with experience or strong interest in natural language processing, machine learning, or health informatics. They should have: 

• The legal right to work in Canada 

• Experience with text classification and NLP methods 

• Ability to work with clinical text data and medical terminology 

• Understanding of or interest in primary care and healthcare delivery 

• Strong analytical and coding skills 

• Ability to work independently and document methods clearly 

• Comfort working with secure health data platforms 

Experience with healthcare consultation systems or clinical text analysis is an asset. 

Anticipated Start: April, 2026 
Duration: 5 months 
Location: Ottawa (Onsite with hybrid working options)

Goal: Develop or adapt predictive models that identify key parameters and generate actionable recommendations for route optimization in public health operations.

Focus: Predictive modeling and route optimization for public health operations

Scope: This project develops predictive models to optimize routes for public health operations using real-world mileage and operational data from public health staff. The intern will apply mathematical modeling, optimization techniques, and machine learning to analyze travel patterns, resource allocation, and workflow constraints.

Work includes determining the most influential parameters affecting route efficiency (travel distance, time windows, priority levels, resource availability), recommending optimal team configurations and task assignments to minimize travel and maximize productivity, and providing insights into how predictive analytics can support scalable, sustainable solutions.

The objective is creating a data-driven foundation for future route optimization tools that improve efficiency, reduce costs, and enhance service delivery. Lessons learned will be applied to improve operational workflows, service offering, and resource allocation in public health programming, and will inform design of a future route optimization software application tailored to public health needs.

The project contributes to building a framework for intelligent scheduling and routing systems that can be integrated into future digital tools, improving responsiveness and reducing environmental impact of public health operations. 

Desired Candidate: Graduate student, early career professional, or a qualified undergraduate student.  experience or strong interest in optimization, operations research, or predictive modeling. They should have: 

• The legal right to work in Canada 

• Experience with mathematical modeling and optimization techniques 

• Familiarity with machine learning and predictive analytics 

• Ability to work with geospatial data and routing algorithms 

• Understanding of or interest in public health operations and service delivery 

• Strong analytical and data visualization skills 

• Ability to translate technical findings into actionable recommendations 

• Experience with mapping APIs (e.g., Google Maps, Azure Maps) or similar tools 

Interest in operational efficiency, resource allocation, or sustainability is an asset.

Anticipated Start: April, 2026
Duration: 3 months
Location: Virtual (no onsite requirement)

Goal: Use AI and data analytics to optimize school communications, staff scheduling, and capacity planning for oral health programs.

Focus: AI and data-driven methods to improve operational efficiency in school communications, staff scheduling, and capacity planning

Scope: This project focuses on enhancing operational efficiency within the Children’s Oral Health Program, which coordinates large-scale screening activities, mobile and fixed clinic operations, and staff deployment across multiple sites using historical datasets identifying unmet oral health needs.

The intern will assess existing workflows for school communications and staff scheduling, explore AI-supported automation of multi-step email processes with tracking and personalization, and analyze historical data to support fair, geography-aware staff scheduling.

Work includes supporting capacity planning by assessing staffing levels against annual screening and service delivery goals, defining requirements and feasibility for a future scheduling or optimization platform, and prototyping or modeling machine learning approaches for scheduling and planning at a conceptual or analytical level.

Desired Candidate: Graduate student, early career professional, or qualified undergraduate, with strong communication skills and experience or interest in data analysis, machine learning, or optimization. They should: 

• Have the legal right to work in Canada 

• Be comfortable working with complex operational datasets 

• Communicate clearly with technical and non-technical stakeholders 

• Have ability to synthesize input from analysts, program staff, and leadership 

• Work collaboratively with an internal Health Analyst and existing optimization efforts 

Anticipated Start: April, 2026
Duration: 6 months
Location: Onsite with hybrid working options.

Goal: Explore AI tools to support and streamline rapid risk assessments for acute public health events.

Focus: Exploratory assessment of AI tools to support PHAC’s Rapid Risk Assessment workflows 

Scope: This internship explores how artificial intelligence tools could support or enhance Rapid Risk Assessments (RRAs) conducted by PHAC during acute public health events. The project will include an initial assessment of how AI tools such as Claude Skills, AI agents, or specialized platform adaptations could streamline risk assessments and explore possible use cases.

The intern will review current RRA workflows to identify pain points, consider integration of existing evidence and expert input, address data security concerns including privileged information handling and unintentional disclosure prevention, develop a prototype tool for testing, and integrate AI capabilities within existing risk assessment frameworks and workflows. 

Desired Candidate: Graduate students only (no undergraduates or early career professionals), with experience in epidemiology and infectious diseases, and an interest in AI applications for public health decision support. They should: 

• Have the legal right to work in Canada 
• Be enrolled in a graduate university degree program.
• Be able to translate technical concepts for non-AI experts 
• Demonstrate strong analytical and communication skills 
• Be available to work remotely, with the option for in-person engagement 

Anticipated Start: April, 2026
Duration: 6 months
Location: Virtual (no onsite requirement)

Desired Candidate: Graduate student, early career professional, or a qualified undergraduate, with strong technical skills in Python and readiness to embrace the complexity of chronic disease modeling and interdisciplinary collaboration. They should have:

• The legal right to work in Canada
• Experience or strong interest in simulation modeling (system dynamics, particle filtering, agent-based modeling, or similar approaches)
• Ability to build and refine model parameters from both quantitative data and literature
• Skills in synthesizing high-quality evidence and incorporating expert opinion appropriately
• Strong data analysis and visualization skills
• Comfort coordinating across clinical and technical stakeholders
• Curiosity, adaptability, and ability to work within a complex systems-thinking environment

Goal: Develop an ethical AI-enabled social listening pipeline to monitor public health concerns and misinformation.

Focus: AI-enabled social listening and infodemic surveillance to support public health preparedness and trust-building

Scope: The internship will explore and develop a feasible, ethical, and documented AI-enabled social listening pipeline to support public health surveillance and preparedness. The intern will assess how publicly available data such as social media data (e.g., Facebook, Reddit) can be responsibly used to identify emerging public health concerns, misinformation, and sentiment related to public health topics, drawing on the WHO infodemic insights framework. Work will include an environmental scan of best practices, assessment of platform-specific data access and constraints, evaluation of ethical considerations and bias, and exploration of AI and generative AI methods for social listening, sentiment analysis, and misinformation detection. The intern will design and internally validate a prototype pipeline, document methods and code, outline risks and limitations, and recommend how findings can be communicated clearly to non-technical audiences. The emphasis is on feasibility, validation, and learning, not real-time intervention or full production deployment.

Desired Candidate: Graduate student, early career professional, or a qualified undergraduate. They should have:

• The legal right to work in Canada

• Interest in public health surveillance, misinformation, and trust in public institutions

• Experience or familiarity with Python, R, Julia, or similar tools

• Clear communication in writing and presentations

• Critical thinking about ethics, bias, and responsible AI use

• Comfort working independently while engaging regularly with supervisors

Backgrounds in data science, epidemiology, biostatistics, communications, or marketing are relevant. Experience with social media analysis or NLP is an asset but not required.

Anticipated Start: April, 2026
Duration: 6 months
Location: Virtual (no onsite requirement)

Goal: Design structured frameworks for privacy impact assessments, project intake, and responsible AI governance.

Focus: AI-supported privacy impact assessments, project intake, and responsible AI governance

Scope: This internship focuses on strengthening how Southwestern Public Health evaluates, initiates, and governs AI-enabled projects. The intern will help design a structured and consistent approach to Privacy Impact Assessments (PIAs) and project intake by developing templates, workflows, decision criteria, and evaluation frameworks. The intern will explore how AI tools (including generative AI and low-code/no-code tools) can support early-stage risk assessment, alignment with organizational priorities, and project prioritization. Work will also contribute to broader AI readiness, including documenting lifecycle stages, identifying governance gaps, and recommending approaches to monitor risk, fairness, accountability, and effectiveness.

Desired Candidate: A graduate student with an interest in AI governance, digital transformation, and responsible innovation. They should: 

• Have the legal right to work in Canada 

• Be highly organized and analytical 

• Be comfortable working across technical and non-technical teams 

• Have experience or interest in project management, process design, or service design 

• Be able to develop templates, workflows, and clear documentation 

• Demonstrate interest in responsible AI principles (privacy, transparency, accountability, risk management) 

• Communicate clearly and professionally in writing and meetings 

Anticipated Start: April – May, 2026
Duration: 6 months
Location: Onsite with hybrid working options

Goal: Prototype AI-enabled solutions to automate workflows, visualize data, and support internal public health operations.

Focus: Applied AI development, prototyping, and analytics to support internal public health operations

Scope: This internship focuses on hands-on AI and data-driven solution development to address operational and organizational challenges within Southwestern Public Health. The intern will collaborate with internal teams to prototype, test, and refine AI-enabled solutions that support staff workflows. This may include automation of internal processes, data visualization, transforming structured information into project timelines (e.g., Gantt-style outputs), and supporting monitoring of AI system performance and risks. The role also contributes to building internal capacity by documenting technical approaches, assessing feasibility, and identifying opportunities for scaling or improvement. The emphasis is on learning, experimentation, and practical impact, rather than enterprise-wide deployment.

Desired Candidate: A graduate student with technical experience in AI or data science. They should: 

• Have the legal right to work in Canada 

• Have experience with Python and/or R 

• Be comfortable working with datasets, pipelines, and model evaluation 

• Have experience or interest in AI/ML prototyping and experimentation 

• Be able to translate organizational needs into technical solutions 

• Understand or be eager to learn about ethical and responsible AI practices 

• Have strong problem-solving skills and a collaborative mindset 

Anticipated Start: April – May, 2026
Duration: 6 months
Location: Onsite with hybrid working options

Goal: Build a machine learning model to predict severe pediatric asthma events using health and environmental data.

Focus: Machine learning prediction of severe pediatric asthma events using health system and environmental data

Scope: This project develops and validates a predictive model to identify children at risk of severe asthma-related emergency department visits using longitudinal administrative health data and spatiotemporally resolved air pollution indicators. The intern will analyze population-normalized pediatric asthma ED visits (ages 2–17) from 2011–2024 using data from the Government of Alberta Interactive Health Data Application and Alberta Health Services administrative database, integrated with air quality data (PM₂.₅, NO₂, O₃, CO) from provincial monitoring networks and satellite estimates. Work includes developing a machine learning model to predict high-risk episodes requiring ED care or admission, integrating individual-level and area-level factors such as acuity scores, rurality, and socio-demographics with environmental exposures, and characterizing health system trends in asthma ED visits, hospitalizations, and presentation severity. The intern will prototype a web-based tool for public health use to map asthma exacerbation risk by region and time, enabling early response and targeted interventions to improve health equity in pediatric asthma care, particularly for vulnerable populations including rural and socioeconomically disadvantaged communities.

Desired Candidate: Graduate student, early career professional, or qualified undergrdauates, with experience or strong interest in machine learning, environmental health, or health data science or early career professional.They should have: 

• The legal right to work in Canada 

• Experience with feature engineering and predictive modeling 

• Familiarity with health administrative data and environmental datasets 

• Understanding of or interest in health equity and pediatric health 

• Strong analytical and data visualization skills 

• Ability to work with large-scale longitudinal datasets 

• Comfort with spatial and temporal data analysis 

Experience with web-based tool development or health surveillance systems is an asset.

Anticipated Start: April, 2026
Duration: 6 months
Hours: 15 hours/week
Location: Onsite with hybrid working options OR Virtual (no onsite requirement)

Goal: Design an evidence-based framework for an AI-supported youth sexual health education tool.

Focus: Exploratory research and design (non-technical)

Scope: The internship will review existing digital and AI sexual health education tools, identify gaps in accessibility, cultural relevance, privacy, and ethical design, and analyze data from CDC/YRBSS, Arkansas Department of Health, and community sources to understand youth needs. The intern will develop an evidence-based framework and concept blueprint for a future AI tool, conduct youth feedback sessions to refine tone and content, and deliver documentation with recommendations for future development.

Desired Candidate: Undergraduate, Graduate student or Early career professional , interested in public health, youth well-being, and responsible technology. They should: 

• Have the legal right to work in the United States of America 

• Have foundational knowledge of public health or health education 

• Be comfortable working with data and research 

• Demonstrate cultural humility when engaging with youth and sexual health topics 

• Translate complex information into clear, age-appropriate insights 

• Work independently while communicating progress effectively 

Relevant backgrounds include public health, health informatics, behavioural science, data science, communications, social work, or related fields.

Anticipated Start: Flexible March-May 2026
Duration: 6 months
Location: Onsite with hybrid working options

Goal: Use machine learning to derive youth health personas from survey data to inform equity-focused interventions.

Focus: Machine learning analysis to derive youth health personas from survey data

Scope: This project applies feature engineering, dimensionality reduction, and cluster analysis to the Well-being and Health Youth (WHY) survey, a biannual instrument with high student participation that captures detailed data on mental health, substance use, belonging, and school climate across multiple cycles.

The intern will derive youth “personas” reflecting overlapping risks and protective factors, beginning with the most recent survey cycle and extending analyses across multiple cycles to examine how personas emerge, shift over time, and differ for equity-deserving groups across gender, sexual orientation, race, disability, and geography. Work will occur within WDGPH’s modern data science environment (Kubeflow deployment) with mentorship from experienced data science and data engineering staff. The intern will gain experience with the NIST AI Risk Management Framework, which WDGPH is adopting for AI governance. Deliverables include refined personas with equity analysis, technical documentation, knowledge translation outputs including dashboard updates and blog posts on NIST AI RMF and clustering methods for public health, and implementation of metadata/versioning approaches for survey data.

Desired Candidate: Snr. Undergraduate, Graduate Student or Early Career Professional in applied machine learning or a related field who wants to work at the intersection of AI and public health. They should have: 

• The legal right to work in Canada 

• Strong skills in Python and Jupyter notebooks (R is an asset) 

• Experience implementing and evaluating clustering and dimensionality reduction methods 

• Familiarity with Git and collaborative coding 

• A clear health equity lens and ability to think critically about impacts on equity-deserving groups 

• Strong collaboration, organization, and communication skills 

• Ability to create clear figures, dashboards, and blog-style communication 

Interest in data standards and metadata (e.g., frictionless data or similar frameworks) is an asset.

Anticipated Start: Flexible
Duration: 6 months
Location: Onsite with hybrid working options

Goal: Apply NLP and machine learning to analyze youth-articulated care goals and understand goal evolution over time.

Focus: Natural language processing and machine learning analysis of youth-articulated care goals

Scope: The internship will analyze self-identified care goals captured through the Goal-Based Outcome (GBO) tool, which allows youth to express goals in their own words rather than through checklists. The intern will apply NLP and topic modeling to large-scale text data from over 6,000 youth across approximately 44,000 visits to identify common goal themes, examine concurrent goals and their relationships, and analyze how goals change throughout treatment. Work will also assess how equity factors and intersectionality shape goal evolution and validate or complement existing manual coding schemes using NLP/ML methods.

Desired Candidate: Graduate student or Early Career Professional, with a strong interest or experience in NLP, machine learning, or text analytics, and an understanding of mental health, youth services, or health equity. They should: 

• Have the legal right to work in Canada 

• Be comfortable working with large-scale text and structured health data 

• Work independently and document methods clearly 

• Be willing to complete required CAMH research training (≈2 weeks) 

Experience with topic modeling, mixed-methods analysis, or learning health systems is an asset.

Anticipated Start: April, 2026
Duration: 6 months
Location: Onsite with hybrid working options