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6 Jan 2026 Job Information Organisation/Company CNRS Department Laboratoire de physique des 2 infinis - Irène Joliot-Curie Research Field Physics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 26 Jan 2026 - 23:59 (UTC) Type of Contract Temporary Job...
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- Programming skills (e.g., Python, Matlab) - Advanced knowledge of statistical analysis (e.g., R) Desirable skills: -Specialization in language development, including research experience with infants and/or
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team • Proficiency in programming languages (e.g. Python, R, Fortran) Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8190-JEARAU-003/Default.aspx Work Location(s) Number
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new pollen analysis • Modeling and Statistics: Utilize GIS, R statistical environment, and other tools for pollen-based modeling (REVEALS and LOVE models) and statistical analysis. • Collaboration: Work
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design, data analysis, and programming (e.g., SPM, MATLAB, Python, R, etc.) Strong scientific writing skills Excellent organizational and communication skills Willingness to work in an interdisciplinary
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processing and synthesizing different types of social and ecological data at various spatial scales, as well as large spatial datasets in R or GIS ■ Experience working with R ■ Experience in socio
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R&T. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UPR3407-FABBEN-004/Candidater.aspx Requirements Research FieldEngineeringEducation LevelPhD or equivalent Research
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(HORIZON-INFRA-2024-TECH-01-01 – R&D for the next generation of scientific instrumentation, tools, methods, and solutions for RI upgrade), this position focuses on data curation activities under Work Package
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data analysis as well as qualitative analysis. Mastery in R and/or Statistica, and experience in database management are required. • Fieldwork experience in the dry forests of Brazil, Argentina and
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, library preparation, cell culture, and imaging - Proficiency in computer languages (bash, python, awk, R) - NGS/omics data analysis - Proficiency in statistics for high-throughput data analysis - Generation