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Associate Professor/Professor -JDMI UHN Chair in Medical Imaging and AI at the University of Toronto
experience in collaboration with a Department of Medical Imaging, prior successful grant applications as PI or Co-PI, patents in the field of AI or machine learning and successful graduate student supervision
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title: CSC2701HY – Communication for Computer Scientists – The Job Hunt; 0.1 FCE (Sections LEC5101, LEC5201, LEC5301, LEC5401) *Please note, this is a 0.5 FCE course co-taught with other instructors
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title: CSC2701HY – Communication for Computer Scientists – Interview Training; 0.1 FCE (Sections LEC5101, LEC5201, LEC5301, LEC5401) *Please note, this is a 0.5 FCE course co-taught with other
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 2 months ago
on the responsible use of AI and machine learning to enhance data quality, automation, and analytical capabilities. Provide technical leadership on data quality, standards, and methodologies across core scanning
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to the development and ongoing stewardship of a welcoming and culturally grounded learning environment through activities that support decolonization of physical spaces and program initiatives. Additionally
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; proficient spelling and grammar skills; proven computer skills including keyboarding and proficiency with electronic medical records and Microsoft Office Software are essential. Confidence in the ability
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and documents lessons learned for future initiatives Monitors service performance, utilization levels, and billing calculations Prepares reports that support service evaluation, planning, and decision
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underpinning fundamentals such as fluid mechanics, dynamics and control with application of recent advances in artificial intelligence, machine learning and multidisciplinary design optimization
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well as part of a team; ability to function under pressure and in stressful situations; ability to evaluate the work of others; sound computer knowledge and proficient with Microsoft products. While applicants
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lead an independent research program in computational oncology, centred on new quantitative and machine-learning methods that integrate single-cell genomics and spatial profiling to study cancer