Praxis Spinal Cord Institute (Praxis) is a Canadian-based not-for-profit organization that leads global collaboration in spinal cord injury research, innovation, and care. We advance ground-breaking ideas that can be put into practice, making lives better. We actively engage people living with spinal cord injuries and other world-class experts to work together to identify and solve the most urgent challenges to make exceptional improvements in the health of people with spinal cord injuries. Praxis Spinal Cord Institute is proudly accredited by Imagine Canada and was named one of Canada’s Top 100 Charities by Maclean’s and Money Sense magazines, achieving an A+ rating.
Through a diverse workforce, Praxis is committed to excellence in research, innovation and care for people living with spinal cord injury. Praxis recognizes that a diverse workforce, comprised of individuals with an array of identities, abilities, backgrounds, cultures, skills, perspectives and experiences is vital to creativity, growth and innovation and our success in making an impact on quality of life. We support our commitment by fostering an inclusive workplace which is fair, equitable, supportive, welcoming and respectful, allowing us to continue to transform health outcomes.
JOB SUMMARY
We are seeking a highly motivated Master's or PhD student with expertise in computational modeling, machine learning, and causal inference to contribute to cutting-edge research by applying causal models in large language models (LLMs) for spinal cord injury (SCI). This position will focus on leveraging advanced AI techniques to analyze disease progression and identify actionable insights for early diagnosis and personalized treatments. This is a 1-year term part-time student role.
JOB ACCOUNTABILITIES
Develop and implement causal modeling frameworks integrated with LLMs for SCI.
Collaborate with interdisciplinary teams to refine models and validate results.
Provide support in manuscript preparation and submission including proofreading and formatting manuscripts.
Regularly monitor projects along with the Project Team, including the planning through implementation to ensure timely milestones and deliverables.
Perform research-related activities including literature search and synthesis, and preparing research documents (protocols, scientific abstracts, conference presentations).
Analyze multi-modal datasets (e.g., clinical, imaging, and biomarker data) using machine learning and statistical methods.
QUALIFICATIONS
Education:
Enrolled in a Master’s or PhD program in Computer Science, Data Science or Biomedical Engineering or a related field.
Strong background in machine learning, causal inference, and computational modeling.
Experience with programming languages such as Python or R; and familiarity with AI frameworks for LLMs.
Experience: Any experience with the job duties described above and/or SCI would be an asset
Skills/Behaviours:
Familiar with literature search method (PICO preferred)
Ability to synthesize evidence from literature review into useful information
Ability to prepare clear, concise, and professional-looking reports
Demonstrated skills in scientific writing as well as versatile lay summaries
Excellent organizational skills in storage and documentation systems (e.g. folder, file name nomenclature)
Adhere to good research practices
Ability to articulate and communicate clearly and professionally
Interpersonal skills and ability to work with discretion, tact, diplomacy individual and as part of a team
Intermediate or advance level with MS Office
An equivalent combination of education, experience and skills/behaviors will be considered.
HOW TO APPLY
Interested and qualified candidates can apply by May 15, 2025. While we thank all applicants for their interest, only short-listed candidates will be contacted. For more information on Praxis, please visit www.praxisinstitute.org.
Praxis Spinal Cord Institute would like to acknowledge that the land on which we are located is on the unceded traditional territory of the Coast Salish Peoples, specifically the shared traditional territories of the S?wx?wú7mesh (Squamish), s?lil?ilw??ta?? (Tsleil-Waututh), and x?m??k??y??m (Musqueam) First Nation
