Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- University of British Columbia
- McGill University
- University of Toronto
- University of Saskatchewan
- University of Waterloo
- BioNano Lab
- Canadian Association for Neuroscience
- Institut national de la recherche scientifique (INRS)
- Nature Careers
- Ryerson University
- Simon Fraser University
- University of Guelph
- 2 more »
- « less
-
Field
-
Sessional Lecturer - AMS402H1F: Interfacing Cultures: AI, Platforms, and Algorithmic Politics Across
algorithmic power, with case studies on cross-border AI development, digital identity politics, and state-platform relations. Drawing from American Studies, Science and Technology Studies (STS), and digital
-
repositories programmed in Python, Pytorch, LangChain using git repo. Develop clean, readable, and maintainable public code using object-oriented programming principles in Java and Python. Apply machine learning
-
The fellow will be responsible for: Building collaborations with our multidisciplinary team (medical physicists, engineers, computer scientists, nuclear medicine physicians) to develop and implement innovative
-
, strings, pointer-based data structures and searching and sorting algorithms. The laboratories reinforce the lecture topics and develops essential programming skills. Estimated course enrolment: ~150
-
scientists, nuclear medicine physicians) to develop and implement innovative AI algorithms applied to medical images To lead effort on enabling translational and physician-in-the-loop AI solutions for medical
-
objective is to develop a next generation of AI approaches that are more sustainable and accessible. Relevant domains include mathematical and computational optimization, learning algorithms, statistical
-
a training dataset for developing machine learning algorithms for increasing the consistency of quality control in two cohort studies: healthy controls and epilepsy patients. Key Responsibilities
-
. The ultimate objective is to develop a next generation of AI approaches that are more sustainable and accessible. Relevant domains include mathematical and computational optimization, learning algorithms
-
, lists, maps. Program structure: control flow, functions, classes, objects, methods. Algorithms and problem solving. Searching, sorting, and complexity. Unit testing. Floating-point numbers and numerical
-
managed, and how health research and discovery is conducted in the coming years. Recent focus on the application of artificial intelligence to health and medicine has primarily been on the development