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of process-based models and data assimilation techniques. Excellent skills in data visualization and communication. Preferred Experience in large-scale ecosystem modeling or GHG inventory analysis. Experience
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% Experiment design and execution. 15% Data analysis and manuscript preparation. 10% Lab management and administration. Qualifications Required Qualifications: Doctoral degree (PhD, MD, DVM) with dissertation
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interest in the use of machine learning techniques to enable new analysis strategies, as well as the application of deep understanding of the detector to enable novel physics studies. The group also has a
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(These examples do not include all possible tasks in this work and do not limit the assignment of related tasks.) 90% Research and data analysis: Individual will design and conduct complex, multi-step laboratory
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in data collection, data analysis, and manuscript compiling for publication in peer-reviewed journals. The successful candidate will be expected to work closely with research collaborators and could be
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documentation, regulatory documents, and case-report forms (CRFs). • Receive and respond to data queries and monitoring action items as assigned. Data Analysis (15%) • Interpret patient questionnaire data
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rust resistance gene network of wheat. Note: These are not positions to which permanent residency can be granted for international appointees. Job Duties: Bioinformatic analysis of plant and pathogen
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diseases, cellular senescence, drug development, DNA damage and repair and molecular therapeutics. Summary of Position: The focus of the research for the position is on the analysis of the effect of immune
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Qualifications: -Must have experience with growing cells in tissue culture, harvesting cells from embryos at different stages of development, isolating cells for single-cell RNAseq analysis in parallel with
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gene expression analysis to study animal models and human immune cells. We are currently exploring the biology of tolerance versus allergy to foreign proteins in animal models and developing a deep