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Intern to support the development, benchmarking, and large-scale profiling of 3D cancer models within our multidisciplinary team. The ideal candidate is enthusiastic about experimental work, data-driven
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development of transferable learned representations from large-scale public omics data and metadata, and the software infrastructure needed to build, maintain, and distribute them at community scale, including
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data collection activities, ability and interest in working with large datasets, and interest in working with interdisciplinary teams of researchers. The expectation is that the scholar would help lead
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techniques using R, demonstrating strong proficiency in data analysis and reproducible workflows manage large health research datasets and data access applications provide statistical advice and mentoring
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LanguagesFRENCHLevelBasic Research FieldAstronomyYears of Research ExperienceNone Additional Information Website for additional job details https://emploi.cnrs.fr/Offres/Doctorant/UMR8254-SYLDES-022/Default.aspx Work
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LanguagesFRENCHLevelBasic Research FieldBiological sciencesYears of Research ExperienceNone Additional Information Website for additional job details https://emploi.cnrs.fr/Offres/Doctorant/UMR9002-WILRIT-003/Default.aspx
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Position Details Position Information Advertising Department 1 CARD SYSTEM Academic Term/Year SUMMER 2026 Position Title Federal Work Study (On-Campus - Office Assistants) Posting Number 08001
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European biodiversity hotspot - for more information look at https://ser-insr.org/news/2017/12/29/grassland-restoration-in-the-white-carpathian-mountains ). Effectiveness Assessment: Quantifying real-world
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French National Research Institute for Agriculture, Food and Environment (INRAE) | Toulouse, Midi Pyrenees | France | 8 days ago
30 Mar 2026 Job Information Organisation/Company French National Research Institute for Agriculture, Food and Environment (INRAE) Department IHAP 1225 INRAE ENVT Research Field Medical sciences
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, harmonization, aggregation and analysis of clinical data for a large ophthalmology repository. Extract information from unstructured electronic health record text using large language models (LLMs) and clinical