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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
Posting Information Posting Details Department Biology - 318200 Posting Open Date 08/01/2025 Application Deadline 08/21/2025 Open Until Filled No Position Type Postdoctoral Scholar Position Title
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to their research. Ideal candidates will be expected to employ state-of-the-art computational tools to analyze phylogenetic, metagenomic and multi-omics data sets generated from different clinical trials. Applicants
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Experience with CRISPR screening and/or gene editing Experience with cancer data analysis and/or analyzing (epi)genomic sequencing data **Please kindly attach a cover letter with your CV. NOTE: Position tasks
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-analysis of the relationship of hydraulic conductivity and vegetation cover. Data analysis and publication writing. Using remote sensing to evaluate cover conditions on rangelands. Other duties as required
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: $62,232 - $81,000.00, depending on qualifications and experience. To apply visit https://academicjobsonline.org/ajo/jobs/30322 and submit a cover letter, full CV, and contact information of three
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: https://www.princeton.edu/acad-positions/position/38801 and include a cover letter, CV, research statement (a discussion of past research, expertise, and research interests), and contact information
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, opportunities for conference participation. Application Procedure Application must include: A detailed CV A cover letter highlighting relevant experience A list of publications Contact information for 1–2
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on qualifications and experience. To apply visit https://academicjobsonline.org/ajo/jobs/30322 and submit a cover letter, full CV, and contact information of three professional references. Applications will be
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Classification Title: Postdoctoral Associate Classification Minimum Requirements: PhD in Computer Science, Geographic Information Science, Data Engineering, or a related technical field. Job
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subseasonal precipitation forecasts. The postdoc will be working to develop a real-time forecasting tool to improve U.S. Week 3-4 precipitation forecasts with innovative data science methods to provide