University of Toronto Postdoctoral Fellowship 2025 in Canada (Fully Funded)

University of Toronto is the top academic university in Canada and is in deep learning, neural networks, and the intersections of AI.

 University of Toronto Postdoctoral Fellowship 2025 in Canada (Fully Funded)

University of Toronto Postdoctoral Fellowship 2025 in Canada (Fully Funded)

Applicant that are interested in taking a brave step in career goal in research and more are invited to apply for the University of Toronto Postdoctoral Fellowship 2025 in Canada. This Fully Funded Fellowship is one of the greatest fellow programs that is set up for talented researchers to pursue their Postdoctoral Degrees at the University of Toronto.


The University of Toronto is the top academic university in Canada and is in deep learning, neural networks, and the intersections of AI and the natural sciences. By equipping them with important AI tools, the Eric and Wendy Schmidt AI in Science Postdocs program at the University of Toronto enhances the work of early-career academics in the fields of engineering, mathematics, and natural science.


University of Toronto Postdoctoral Fellowship 2025 Summary:

  • Offered by: University of Toronto
  • Degree Level: Postdoc
  • Fellowship coverage: Fully Funded
  • Eligible nationality: All Nationalities
  • Award country: Canada
  • Last Date: 15 October 2024


Financial Benefits:

  • A salary of $85,000 CDN per year.
  • Comprehensive benefits as part of the fellowship package.
  • A package to support fellows in their research and professional development.


Academic Fields:

  • Applied Science
  • Chemistry
  • Computing
  • Earth Science
  • Engineering
  • Environmental Science
  • Life Science
  • Mathematics
  • Physics & Many More


Documents Required:

  • Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship Application Form
  • Research Proposal (3 pages)
  • Bibliography (1-page)
  • AI in Science Training Plan (1-page)
  • Candidate Statement (2 pages)
  • Significance of Leadership Contributions (1-page)
  • Applicant’s CV
  • Two confidential letters of reference (2 pages per letter)
  • Supervision Letter


Eligibility Criteria:


  • Candidate must have  leadership skills in the field of research and academic performance.
  • There must be strong collaboration between the candidate, their supervisor(s), and the academic setting for research.
  • There must be the possibility of effectively applying AI research techniques.
  • Fulfillment of doctoral degree requirement. 
  • For applicants who had significant career delays within two years of completing their PhD due to medical conditions, parental leave, family obligations, or research slowdowns or shutdowns brought on by the COVID-19 epidemic, the eligibility term has been extended to January 2020.
  • International applicants: finish your PhD before the start date. Citizens and permanent residents of Canada: finish your PhD within six months of starting.
  • No postdoctoral fellowship with the Eric and Wendy Schmidt AI in Science fellowship running simultaneously. It is necessary to resign from the current postdoctoral fellowship to accept the award.
  • Candidate should possess a degree in one of the natural sciences or engineering (NSE) fields, such as mathematics or computer science. NSERC Research Subject Codes provide details about NSE disciplines.
  • For a full-time appointment at the University of Toronto, either for status-only work or funding. A co-supervisor with AI experience is necessary if the primary supervisor is new in the field.


Application Deadline:

15th October 2024. 


duration 

 2 years.


How to Apply:

Visit the Official Link below

Official Link: https://schmidtfellows.utoronto.ca/apply


For more scholarship opportunities visit:

https://opportunitieshaven.com


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Opportunities Haven : University of Toronto Postdoctoral Fellowship 2025 in Canada (Fully Funded)
University of Toronto Postdoctoral Fellowship 2025 in Canada (Fully Funded)
University of Toronto is the top academic university in Canada and is in deep learning, neural networks, and the intersections of AI.
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