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machine learning computer models (i.e. algorithms) for medical imaging, bioinformatics (i.e genomics data including single cell and spatial omics) and drug development applications. Performs analysis
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building on a successful process already developed by Salfi’s team in the QMI Nanofab facility [1]. Experimentally investigates and optimizes the performance of novel multi-quantum dot circuits and/or
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analytics and implementation of algorithms in care settings along with clinical, business and ethical challenges will be explored. In addition, an overview of the issues within the health industry
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, developing, and implementing innovative machine learning models and algorithms to drive insights from the hEDS*omics multimodal dataset, encompassing clinical, environmental, and multi-omics data. This role
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: COMP 250 - Course Title: Introduction to Computer Science Course Number: COMP 251 - Course Title: Algorithms and Data Structures Course Number: COMP 252 - Course Title: Honours Algorithms and Data
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Introduction to Electric Power Systems EE271 Materials and Heat Transport in Electrical Engineering EE342 Transmission of Electrical Energy EE365 Algorithms and Circuits with Finite Precision Arithmetics EE382
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Consortium (AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community
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analytics, and software platform development for industry funded applied research projects. Teamwork with experts in innovative and novel technological solutions on industry challenges. Skills & Abilities
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of developing algorithms that are both technically robust and clinically relevant, ensuring that these innovations can be integrated seamlessly into existing imaging systems and workflows. Collaborating with
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applications across a wide range of imaging and video processing fields beyond medical imaging. The Research Associate will be at the forefront of developing algorithms that are both technically robust and