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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 4 days ago
benefits and protection from harms. Applicants must have earned a PhD degree in Statistics, or a related area (such as Computer Science, Data Science, Quantitative Social Science, Sociology, Economics
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interdisciplinary in nature, including mathematical biology, mathematical geosciences, applied partial differential equations, environmental and spatial statistics, scientific computing, and the scholarship
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learning, statistics, data science, applied math and/or other quantitative backgrounds who are enthusiastic about bringing their expertise to address fundamental problems in biology and medicine using
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on this position will be the development and implementation of novel statistical and computational methods with applications to system biology problems and spatial transcriptomics datasets. The work will involve
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Environmental science » Earth science Mathematics » Statistics Researcher Profile Established Researcher (R3) Country Netherlands Application Deadline 31 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job
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in bioinformatics of spatial transcriptomics, scRNA seq Proficiency in statistical methods for data analysis, including a strong understanding of statistical principles Solid foundation in Biological
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for coastal ecosystems. You will perform statistical analyses of time series and spatial data on ecosystems and human activities and participate in more holistic analyses of socio-ecosystems. You will also
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, Statistics, Bioinformatics, Applied Math, or related field. MS or PhD degree is preferred but not required. Required qualifications ? Substantial expertise in training deep learning models and tuning large
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of large spatial and temporal biodiversity datasets. Since almost 20 years the Rhine-Main-Observatory (RMO; https://www.senckenberg.de/rmo/ ) has been part of the German LTER network (https://www.ufz.de/lter
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Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods