The Laboratory of Behavioural Neuroendocrinology at the Centre for Addiction and Mental Health (CAMH) is seeking a full-time, contract (12 months) Scientific Associate to support research on sex and gender differences in brain health, mental health, aging, and neurodegenerative disease. Reporting to the Principal Investigator (PI), the Scientific Associate will provide scientific and computational expertise, bridging computational methods with diverse research data, including raw preclinical and large-scale clinical, population-based, neuroimaging, and longitudinal datasets.

The successful candidate will apply computational research methods, machine learning, data science, and statistical modelling to organize, integrate, process, and analyze complex datasets.

Experience investigating sex differences and/or sex-specific factors in biological or health research is strongly preferred, along with experience working with preclinical data and experimental research.

Responsibilities will include programming, data preprocessing and quality control, data integration, statistical and machine learning modelling, visualization, and reproducible workflows.

The Scientific Associate will contribute to research projects, secondary data analyses, grant proposals, manuscripts, presentations, and knowledge translation, while providing mentorship to trainees and research staff. The role will support high standards of scientific integrity, data governance, reproducibility, and responsible use of computational methods.

This position is primarily based at the CAMH 250 College Street Site, with flexibility for hybrid work arrangements in accordance with operational requirements.

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.

Qualifications

PhD in Neuroscience, Data Science, Biostatistics, Epidemiology, Psychology, Biomedical Sciences, Health Sciences, or a related discipline, with a 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, including proficiency in R, Python, or equivalent analytical programming languages.

Demonstrated experience working with complex preclinical research data and experimental datasets, with an 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 and experience preparing manuscripts, grant applications, research reports, presentations, and knowledge translation materials.

Demonstrated leadership, mentorship, communication, and project management skills, with experience collaborating across multidisciplinary teams and mentoring trainees or research staff. Knowledge of research ethics, data governance, privacy, and responsible research practices is required.

Compensation & Benefits:

- Salary is competitive and based on experience, with a hiring range of $93,822.73 – $117,278.41 per year.
- Employees in this role may progress within the full pay range of $93,822.73 – $140,734.09 per year
- CAMH’s Total Rewards: Includes participation in HOOPP defined benefit pension plan, adaptable work arrangements, and ongoing professional development support.

This role allows professionals to apply their expertise in a mission-driven environment dedicated to public health outcomes.



CAMH is a fully affiliated teaching hospital and research institute of the University of Toronto. As a CAMH employee, you will contribute to our mission by supporting teaching, research, and clinical care across the hospital.

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. Thank you to all who apply; however, only those selected for an interview will be contacted. If you require accommodations during the application or recruitment process, please let us know.

CAMH est un hôpital universitaire et un institut de recherche pleinement affiliés à l'Université de Toronto. En tant qu'employé de CAMH, vous contribuerez à notre mission en soutenant l'enseignement, la recherche et les soins cliniques à travers l'hôpital.

CAMH est dédié à l'équité, à la diversité et à l'inclusion. Notre engagement est de favoriser un environnement de travail, d'enseignement et d'apprentissage qui soit inclusif, respectueux et exempt de discrimination ou de harcèlement.

CAMH encourage fortement les candidatures de candidats qui reflètent la diversité des communautés que nous servons, y compris les Premières Nations, les Métis et les Inuits; les communautés noires et autres communautés racialisées; les communautés LGBTQ2S+; les femmes; et les personnes en situation de handicap, y compris celles ayant une expérience vécue des défis en matière de santé mentale et d'usage de substances.

Nous accueillons les candidatures de toutes origines. Merci à tous ceux qui postulent ; cependant, seuls les candidats sélectionnés pour un entretien seront contactés. Si vous avez besoin d'aménagements pendant le processus de candidature ou de recrutement, veuillez nous en informer.

Through its core values of Courage, Respect and Excellence, CAMH is implementing its Strategic Plan: Connected CAMH, to transform lives, ignite innovation and discovery, revolutionize education and drive social change. CAMH is more than a hospital, it is a cause. 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.

To view our Land Acknowledgment, please click here.

📌 Scientific Associate - Pre-Clinical Research (Toronto)
🏢 CAMH
📍 Toronto