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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 11 hours ago
Responsibilities - Perform quantitative data analysis, using both statistical and machine learning techniques. Prepare operating grant, fellowship and ethics review applications, including developing research
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analyses of these data for research, quality improvement and surveillance purposes. The incumbent will apply appropriate methods to aid in the creation of a learning health system around opioid-related
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analyses of these data for research, quality improvement and surveillance purposes. The incumbent will apply appropriate methods to aid in the creation of a learning health system around opioid-related
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond
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development and target discovery challenges. Qualifications: PhD in bioengineering, computational biology, machine learning, systems immunology, or related discipline, obtained within the last 5 years, by
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discovery challenges. Qualifications: PhD in bioengineering, computational biology, machine learning, systems immunology, or related discipline, obtained within the last 5 years, by the time of
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: Computer Architecture Algorithms and Optimization Health Research Human-Computer Interaction Machine Learning and ML Foundations Machine Perception Natural Language Processing (including Information
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Vision conferences, CVPR, ICCV, ECCV and peer reviewed journals. Minimum Qualifications: PhD in Computer Science or a related field obtained within the last five years. Strong skills in machine learning
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workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness
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experience of the candidate At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and