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at: https://system.erecruiter.pl/FormTemplates/RecruitmentForm.aspx?WebID=ac814ccb0e72406bb4c941d183f388c1 VI. The submission deadline is 02.04.2026 Eligibility criteria VII. Selection of candidates
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the Rosaceae, Citrus, Vaccinium and Pulse crop databases to meet community demand for resources with usable big data aggregated, analyzed, integrated, and visualized, as well as data management and analysis
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; phone: +45 2498 4101). Application deadline The application deadline is January 25, 2026, at 23.59 hrs. CET. Apply online https://fa-eosd-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en
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Classification Title: PhD in Bioinformatics, Computational Biology, Biomedical Engineering, Data Science, or a related field. Classification Minimum Requirements: PhD in Bioinformatics
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5 Feb 2026 Job Information Organisation/Company Université de Limoges Department DRH-RECHERCHE Research Field Educational sciences Literature Cultural studies Researcher Profile First Stage
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be able to manage complex and ad‑hoc datasets, produce high‑quality visualizations, and conduct advanced analyses across both projects. Be Bold. Position Description: One position is available
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 14 hours ago
. Description: This project aims to develop a next-generation wildfire risk assessment platform that tightly integrates Earth Observation (EO) data, deep learning, and dynamic fire behavior modeling
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environments such as All of Us Research Program, Terra or Google Cloud Platform (GCP) Integrate multimodal datasets (EHR + genomics and other omics) Lead data cleaning, pipeline development, visualization, and
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, Slurm, etc.). - Strong skills in Python for data analysis and visualization. - Clear communicator with proven ability to write, synthesize, and present scientific results. Website for additional job
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Methods and Dynamics (MMD) Group at Oak Ridge National Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics