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Koziarski Lab - The Hospital for Sick Children | Central Toronto Roselawn, Ontario | Canada | about 1 month ago
machine learning-related discipline. Strong publication track record in top ML, computational chemistry, or computational biology conferences and journals. Proficiency in Python, demonstrated through
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in the laboratory QUALIFICATIONS Successful applicants will have: PhD with expertise in cancer biology, proteomics and/or computational biology / machine learning A proven track record with first
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PhD in ecology, remote sensing, environmental sciences, or a related field is required at the time of appointment. Experience: The ideal candidate will have a track record of working independently
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: image processing, machine learning, and patient records. Track record of development and implementation of novel machine learning algorithms in the healthcare setting or other spaces. Extensive experience
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decided on in consultation with the supervisor, but may include: Project management, including setting, tracking and evaluating project goals Establishing collaboration, both internal and external
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, policy, and others. Strong communication and interpersonal skills A developing track record of high-quality publications Proven ability to manage research projects and partnerships Capacity to work
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applicants from diverse disciplinary backgrounds, including technical fields, humanities, education, policy, and others. Strong communication and interpersonal skills A developing track record of high-quality
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technical fields, humanities, education, policy, and others. Strong communication and interpersonal skills A developing track record of high-quality publications Proven ability to manage research projects and
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track record of high impact discoveries in cancer genomics and translation to human studies (aparicio.molonc.ca ), including biotech company creation. Our environment is a fast paced multidisciplinary lab
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develops mitigation strategies or resolutions. Maintains study documentation, including tracking and monitoring changes (adaptations) to strategies throughout the implementation process. Completes data