Scientific Associate

1 day ago

Toronto, Ontario, Canada CAMH Full time

Job Description
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:
To view our Land Acknowledgment, please click
here
.
The Geriatric Mental Health Research Services (GMHRS) at the Centre for Addiction and Mental Health (CAMH) is seeking a casual, Scientific Associate to provide computational support (with a focus on ML, Signal Processing, Entropy) in the analysis of biomarkers data, in particular EEG data.

The candidate will report to the Research Operations Manager of GMHRS. The position will require the individual to apply advanced programming skills (in R/Python) to process and analyze large datasets, while working collaboratively with interdisciplinary teams. The successful candidate will be expected to assist with study start-up (e.g., supporting ethics submissions, writing study protocols), work closely with GMHRS personnel to compile sensitive participant data, analyze data, co-supervise students and junior staff, produce manuscripts, and assist with grant writing. The candidate will support a workplace that embraces diversity, encourages teamwork, and complies with all applicable regulatory and legislative requirements. This position is primarily located at 1025 Queen Street West campus.

Job Requirements

  • The successful candidate must have a PhD in Psychology/Biostatistics, Computer Science, or in a relevant field
  • A minimum of at least one(1) years relevant research experience
  • Extensive experience analyzing EEG data with preference given to candidates who have employed methods in EEG analyses
  • Demonstrated familiarity with analysis tools and methods
  • Advance programming skills in R/Python, with demonstrated familiarity with other analysis tools and methods
  • Experience with EEG in geriatric and neuodegenerative diseases populations is highly desired
  • A proven track record and aptitude in scientific publishing
  • The candidate is expected to have previously carried out and published research related to EEG and signal processing with evidence of publication in high-impact journals
  • The candidate must have excellent verbal and written communication skills
  • Experience supervising staff and students
  • Ability to collaborate effectively with colleagues and the scientific community

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