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About the Role
This role supports research on sex and gender differences in brain health, mental health, aging, and neurodegenerative disease by providing scientific and computational expertise. The Scientific Associate will bridge computational methods with diverse research data, including preclinical and large-scale clinical, population-based, neuroimaging, and longitudinal datasets.
Responsibilities
- Lead and contribute to research projects examining sex and gender differences in brain health, mental health, aging, and neurodegenerative disease.
- Bridge computational research methods with diverse research data, including raw preclinical datasets and large-scale clinical, population-based, neuroimaging, and longitudinal datasets.
- Conduct secondary data analyses, including data management, integration, harmonization, quality assessment, statistical modelling, and interpretation of complex datasets.
- Develop and apply data science, machine learning, and computational methods, including programming, data preprocessing, feature engineering, modelling, visualization, and reproducible analytical workflows.
- Apply computational and statistical approaches to identify sex-specific risk factors, biomarkers, and trajectories and support translational research in women’s health.
- Prepare and contribute to grant applications, scientific manuscripts, reports, presentations, and knowledge translation materials, and support collaborative interdisciplinary research initiatives.
- Mentor and provide guidance to trainees and research staff in research methodology, programming, data analysis, scientific writing, and professional development.
- Support responsible research practices through data governance, research ethics, reproducibility, privacy, and responsible application of computational and machine learning methods.
Requirements
- PhD in Neuroscience, Data Science, Biostatistics, Epidemiology, Psychology, Biomedical Sciences, Health Sciences, or a related discipline.
- Minimum of one (1) year of postdoctoral training in an academic, hospital, research institute, or equivalent research environment.
- Demonstrated expertise in computational research methods, data science, and statistical analysis.
- Proficiency in R, Python, or equivalent analytical programming languages.
- Demonstrated experience working with complex preclinical research data and experimental datasets.
- Understanding of experimental design, animal models, and biological context.
- Demonstrated experience working with large-scale and/or multimodal datasets, including clinical, population-based, longitudinal, neuroimaging, administrative, or biobank data.
- Experience with machine learning, statistical modelling, data integration and harmonization, data preprocessing and quality control, feature engineering, visualization, and reproducible computational workflows.
- Demonstrated experience investigating sex differences and/or sex-specific factors in biological, neuroscience, health, aging, or related research.
- Experience in women's health, menopause, cognition, Alzheimer's disease, dementia, or neurodegenerative disease is an asset.
- Strong record of scientific productivity, including peer-reviewed publications.
- Experience preparing manuscripts, grant applications, research reports, presentations, and knowledge translation materials.
- Demonstrated leadership, mentorship, communication, and project management skills.
- Experience collaborating across multidisciplinary teams and mentoring trainees or research staff.
- Knowledge of research ethics, data governance, privacy, and responsible research practices is required.
Skills
- Computational research methods
- Data science
- Statistical modelling
- Machine learning
- Programming (R, Python, or equivalent)
- Data preprocessing
- Data quality control
- Data integration
- Statistical analysis
- Data visualization
- Reproducible workflows
- Scientific writing
- Leadership
- Mentorship
- Communication
- Project management
- Data governance
- Research ethics
- Privacy
- Responsible research practices
Location
- 250 College Street Site, Toronto
- Hybrid
Work Type
- Full-time
- Contract (12 months)
- Hybrid
Experience Level
- Postdoctoral training (minimum 1 year)
Education Level
- PhD in Neuroscience, Data Science, Biostatistics, Epidemiology, Psychology, Biomedical Sciences, Health Sciences, or a related discipline
Salary/Compensations
- Hiring range: $93,822.73 – $117,278.41 per year
- Full pay range: $93,822.73 – $140,734.09 per year
Benefits
- HOOPP defined benefit pension plan
- Flexible work arrangements
- Ongoing professional development support
About the Company
- The Laboratory of Behavioural Neuroendocrinology at the Centre for Addiction and Mental Health (CAMH) is seeking a Scientific Associate.
- CAMH is a fully affiliated teaching hospital and research institute of the University of Toronto.
- CAMH is dedicated to equity, diversity, and inclusion.
- CAMH is implementing its Strategic Plan: Connected CAMH, to transform lives, ignite innovation and discovery, revolutionize education and drive social change.
- CAMH is on a mission to change the way society thinks about and responds to mental illness.
- They aim to eliminate prejudice and discrimination and shape a world where mental illness is central to our healthcare system – a world where Mental Health is Health.
- To learn more about CAMH, please visit their website at: www.camh.ca.
Equal Opportunity
- CAMH is dedicated to equity, diversity, and inclusion. Our commitment is to foster a workplace, teaching, and learning environment that is inclusive, respectful, and free from discrimination or harassment.
- CAMH strongly encourages applications from candidates who reflect the diversity of the communities we serve, including First Nations, Métis, and Inuit Peoples; Black and other racialized communities; LGBTQ2S+ communities; women; and people with disabilities, including those with lived experience of mental health and substance use challenges.
- We welcome applicants from all backgrounds.
- If you require accommodations during the application or recruitment process, please let us know.