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Field
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-fellowships-2025-nev-usp-october-6-20/ . This opportunity is open to candidates of any nationality. The selected candidate will receive a FAPESP Post-Doctoral fellowship in the amount of R$ 12,570.00 monthly
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presentations using Stata (preferred) or R. Lead, draft, and publish papers in peer-reviewed journals. Give oral presentations to disseminate research findings FLSA Exempt Grade Salary Details $75,000 Minimum
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experience with in vivo cancer murine models (breast cancer and/or lung cancer) and flow cytometry Desirable: Previous experience in transcriptomic analysis (scRNAseq), scripting languages (R) and Seurat
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of Limerick (UL) The University of Limerick is committed to the Open, Transparent and Merit-Based Recruitment (OTM-R) of Researchers. The University of Limerick Policy for the Recruitment of Staff which
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). ** About the Fellowship: The selected candidate will receive a FAPESP fellowship with potential benefits including: * Initial funding for 2 years * Annual fellowship of R$110K * Overhead for travel expenses
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proficient in R (ability to handle large data base and statistical analyses). Required Qualifications Ph.D. in Ecology, Evolutionary Biology or a related field. Completion of a doctoral degree in the
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effectively communicate research findings. Desired Qualifications • Experience working with phytoliths. • Experience with fieldwork and laboratory work. • Experience with statistical analyses using R software
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results. Collaborate with senior researchers at EEWRC to align EMS development with ongoing research on distributed energy systems and digital twins. Support collaborative R&D projects involving external
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling