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, numerical method, and data-driven/machine-learning approaches, including: · Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis; · Data-driven and machine
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12-month, 100%-time postdoctoral associate. This position is intended for candidates with strong training in quantitative epidemiology, data analysis, and population-level disease research, who are
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Requirements PhD in Plant pathology, molecular biology, genetics, bioinformatics, or a closely related field. Desired Qualifications Experience with omics data generation/analysis and bioinformatics tools
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, computational data analysis, and single-molecule approaches would be very beneficial but are not required. Equal Employment Opportunity Statement All qualified applicants will receive consideration for
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Publication track record in inter- and trans-disciplinary scholarship Strong skills in qualitative data collection and analysis Strong written and oral communication skills, including journal articles
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. Bioinformatics analysis of data will include use of population genomics techniques, phylogenetics and machine-learning development. The successful candidate will be expected to write up results as manuscripts
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field by the start date. Demonstrated quantitative data analysis experience and peer-reviewed publication record appropriate to career stage. Desired Qualifications Experience in musculoskeletal
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, statistics, or a related field. Minimum Requirements Experience using chlorophyll fluorescence using PAM fluorometry, data analysis using mathematical models, and comfort with coding to control custom
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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to
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independent and original nuclear science research Strong analytical and problem-solving skills Thorough knowledge of nuclear science Proficiency in experimentation, data analysis, and simulation