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statistical and quantitative modelling. The successful candidate will contribute to projects on flow-ecology relationships and habitat suitability modelling for freshwater organisms and will be comfortable
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also collaborate with student research assistants and postdoctoral associates. Data for these projects is accessed through the Federal Statistical Research Data Center (FSRDC), which requires
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society are required. Knowledge and skills of GIS and spatial studies are considered advantageous. To apply, please submit your application at https://careers.purdue.edu and include the following materials
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disparities, and/or population aging. Candidates giving attention to spatial disparities or dimensions of these issues are encouraged to apply, though it is not a requirement. The successful candidate will also
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-dimensional catalog of approximately 30 million galaxies to analyze their spatial distribution and statistical properties, measuring the fundamental cosmological parameters that govern the evolution
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, validate, and operationalize advanced spatial and spatio-temporal statistical methods for mapping and analysing neglected tropical diseases (NTDs). These methods will enhance the precision of disease
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and statistical consulting from experimental design to publication, resulting in close collaborations with experimentalists in every stage of the way. We have an outstanding record of publications in
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the occupational risks faced by inhabitants of the Roman Empire influenced their choice of preferred cults. The main methods used in the project include spatial analysis, predictive modelling, statistics, and the
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spatial transcriptomics derived from rodent models or human samples. o Oversee data processing, quality control, and computational analysis to ensure high accuracy, reproducibility, and adherence to best
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 4 hours ago
, although candidates are expected to have established strengths in spatial statistics and/or applied machine learning. Grounded in the CERC-NEST goal of connecting statistics with knowledge co-production