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Posting Details Position Information Job Title Neuroscience Assistant Professor - Tenure Track About the University It’s your time to shine! Work where the world comes to create, discover and learn
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. Requirements include research experience and an understanding of Bayesian inference, spatial statistics, network analysis, and excellent written and oral communication skills. Proficiency in R statistical
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(EPIB 507) Fall 2025 EPIB 507 Biostatistics for Health Sciences (3 credits) Fall 2025 Epidemiology and Biostatistics: Basic principles of statistical inference applicable to clinical, epidemiologic, and
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | 1 day ago
& Key Responsibilities Applicants are invited to apply for a post-doctoral opportunity in biostatistics, with an emphasis on Bayesian spatial modeling and disease mapping. The successful candidate will
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epidemiology. Strong statistical background, including knowledge of Bayesian statistics and mathematical modeling techniques. Ability to code complex analyses in R, including data visualizations. Excellent
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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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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
modeling, ordination (principal component & factor analysis) and classification (cluster & discriminant analysis) methods, and basic concepts of Bayesian analysis. Emphasis will be placed on how
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(astro)statistics techniques and hierarchical Bayesian inference. The successful candidate will be expected to pursue innovative and independent research, and to establish an outstanding, competitive, and
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Studies Course description: The use of proxy data (terrestrial and aquatic microfossils) to infer past environmental conditions. The nature and extent of Quaternary environmental change is considered in
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the least squares linear regression model in depth and may introduce models for discrete dependent variables as well as the maximum-likelihood approach to statistical inference. Emphasis on the assumptions