35 computational-model Postdoctoral positions at University of Minnesota in United States
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with innovative modeling methods and data analytics methods and spur cross-discipline development between the team in both water resources and computer science. Specifically, the research projects
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analysis of data including measures of pupil dilation, microsaccades, and behavioral measures of speech perception. Experience with data collection and statistical modeling of time-series data are essential
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% – Conduct computational modeling and/or analyze data from clinical and preclinical studies related to neurological conditions (e.g., epilepsy, chronic pain, autonomic dysfunctions) 30% – Develop grant
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, Molecular Biology, or a closely related biomedical field • Experience with retinal immunopathology, photoreceptor biology, or RPE-related degenerative disease models • Demonstrated expertise in retinal
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to their research. Ideal candidates will be expected to employ state-of-the-art computational tools to analyze phylogenetic, metagenomic and multi-omics data sets generated from different clinical trials. Applicants
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modeling. 80% research - The project focuses on developing theoretical models using optimization and information theory to improve understanding of plant hydraulic regulation at the leaf, plant, and
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tissue culture, experimental virology, transcriptome analyses, and immunologic assays. Prior experience conducting relevant experiments using in vitro and in vivo models of infection, such as flow
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and their application in animal models. There will be opportunities to lead a team of students, contribute to grant writing, engage in professional development, and disseminate results at conferences
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our ongoing research, our department members contribute to the growing understanding of how students learn; through their teaching and outreach, we model the implementation of evidence-based educational
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with mouse models of neurodegenerative diseases, demyelinating conditions, and brain tumors • Expertise in flow cytometry, including high-dimensional (spectral) flow cytometry for immune phenotyping