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experience in optimization, machine learning, control systems, or robotics is desirable. No other specific qualifications beyond and a willingness to learn within an interdisciplinary team. If you have any
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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 students. Our commitment to employment equity helps
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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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diverse 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
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 21 days ago
Mississauga (UTM) invites applications for a full-time tenure stream appointment in the areas of Machine Learning, Applied and Theoretical Statistics. The appointment will be at the rank of Assistant Professor
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candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc. Tasks include
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approaches in research design and data analysis, including: Advanced biostatistics and epidemiology, Applications of artificial intelligence and machine learning, and Leveraging diverse data sources (e.g
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experience in optimization, machine learning, control systems, or robotics is desirable. No other specific qualifications beyond and a willingness to learn within an interdisciplinary team. If you have any
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Electrical & Computer Engineering, East Tennessee State University, and the Ludwig Maximilian University of Munich. Completion of a research-based master’s degree is required prior to commencing the PhD
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for this position are as follows: PhD in Forest Ecology, Entomology, or a closely related field, with a focus on geospatial modeling, invasive species dynamics, and applied machine learning for pest risk assessment