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. The Assistant/Associate/Full Teaching Professor will develop course content, prepare academic activities and course delivery materials to support course objectives, advise students, work collaboratively to
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mentorship. The goal is to work together with other researchers, students, and staff, to help the applicant and PI mutually achieve their career objectives in a supportive, non-toxic environment. This is a two
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, such as: Characterizing geographic differences in infection risk of HPAI in domestic animals. Exploring response options, such as vaccination or surveillance strategies. Learning Objectives: The fellowship
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fluid or as individual particles; moreover, complex chemical reactions can occur between species in the plasma. Modelling a plasma is accordingly a very complex and challenging task. The objective
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activities and course delivery materials to support course objectives, advise students, work collaboratively to identify instructors, and perform service to the unit, university, and profession
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high
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of economically valuable species and invasive species that may compete with economically valuable species or promote wildfire. Learning Objectives: The postdoctoral fellow will have the opportunity to apply
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modern clinical trial design, such as Bayesian Adaptive Clinical trial design or established expertise in statistical methods such as structural equation modeling, causal data analysis. Experience in
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 3 months ago
(UTSC) invites applications for a full-time tenure stream position in the area of Statistical Sciences, with a focus on the theory of Bayesian statistics and uncertainty quantification. The appointment
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of ABM accuracy (e.g., Gaussian process emulation, Bayesian calibration methods, etc.). B4. Knowledge of epidemiological, statistical and microsimulation methods, and their strengths and weaknesses