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habitat or alternative food sources. Be familiar with recent developments on the introduction and spread of two spot cotton leaf hoppers in cotton crops. Have experience with methods for data analysis and
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of the relevant fields. Preferred skills: Previous experience in computational ecology and statistics. R or Python. Statistical analysis tools such as NIMBLE, JAGS or STAN. Familiarity with data processing, quality
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and incorporation into a clinical diagnostic lab under ISO-1705 procedures. The fellow will learn and contribute to the experimental design, data analysis and troubleshooting. The fellow will present
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(e.g., enhancers, promoters) in cattle. Customize bioinformatics pipelines for automated processing and analysis of short- and long-read sequencing data. Apply computational approaches to detect genetic
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bioinformatics tools, pipelines, and statistical methods for the analysis of large-scale genomic and transcriptomic datasets ('big data'), with hands-on experience in high-performance computing (HPC) environments
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expected to learn both independently and collaboratively within a multidisciplinary research team, contribute to experimental design and data analysis, publish findings in peer-reviewed journals, and
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climate. Observational work would include data from GRACE, SMAP, GPM or in-situ stations. Model diagnosis and analysis can include CLM, VIC, CLSM, and LIS frameworks including GLDAS and NLDAS. Model
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of the relevant fields (e.g. Ecology, Forestry, Climatology, Geography). Degree must have been received within the past four years, or anticipated to be received by 9/30/2026. Preferred Skills: Data analysis and
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institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences, and
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institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences, and