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statistical analyses including generalized linear model, multilevel modeling, data mining, survey methodology and Bayesian influences. (Required) Demonstrated experience working on collaborative research
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computing, data pipelining, applied statistics, robotics, Bayesian estimation, SLAM Applicant must have a dynamic skill set, be willing to work with new technologies, be highly organized and capable
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
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include Bayesian data analysis, nonparametric statistics, functional data analysis, spatio-temporal statistics, and machine learning/artificial intelligence. Many of our projects involve dynamic processes
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in a previous PhD project. In addition to electromagnetic geophysics, the candidate is expected to contribute to the development of novel workflows for joint inversion of multiple data types (e.g
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, or are there multiple distinct strategies and mechanisms? As a PhD candidate, you will systematically study inter-individual variability in behavior and brain responses, using both online and lab-based paradigms
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multiple locations across several targeted intervals. These will form the basis for empirical calibrations that we will use to predict the hypothetical natural baseline of Earth’s climate, free from
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independently, manage multiple tasks, and communicate findings clearly is essential. About the Project: Novel GM interventions for mosquito control could represent a step change in progressing towards elimination
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, such as records of multiple or intermediate causes of death and linked patient or diagnosis data, to study patterns in cause co-occurrence across death and disease. These shared data sources connect
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. Experience leading investigations linking simulations to observational data. Experience with statistical characterization of data, preferably within a Bayesian framework. Job Description: A Post-doctoral