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clustering, redshift-space distortions, weak/strong gravitational lensing, and artificial intelligence/machine learning (AI/ML). The observational focus is on optical sky surveys (DES, DESI, Roman, Rubin Obs
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
the nation for federal research expenditures as well as for federally funded social and behavioral sciences research and development. Here at Carolina, our highly skilled postdocs play a vital role in our
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machine learning models that predict soil health and crop performance. The position will exploit datasets integrating biochemical and molecular soil parameters (with a focus on microbiome features from
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-analysis, regression modelling, machine learning). You have a solid basis in at least one common high-level programming language (e.g. R, Python). You enjoy collaborative research in international
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machine learning methods is required. You are expected to have a proven track record of publishing scientific results in peer-reviewed journals. What we offer As well as the exciting opportunities this role
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or pre-printed at least one primary research paper as first or co-first author You have some experience in experimental work Desirable but not required/ Nice to have A strong foundation in machine learning
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and refined our pioneering AI-driven methods. This project focuses on improving protein structure prediction, design, quality assessment, and dynamics using innovative machine learning techniques. You
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Researcher (R2) Positions Postdoc Positions Country Ireland Application Deadline 20 Jan 2026 - 23:45 (Europe/Dublin) Type of Contract Temporary Job Status Full-time Hours Per Week 39 Is the job funded through
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Postdoctoral Research Associate (Electrical and Computer Engineering) Posting Number req24681 Department Electrical and Computer Engr Department Website Link https://ece.engineering.arizona.edu/ Location Main
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environment The Brain Imaging & Neuro Epidemiology group (BraINE) develops advanced methods for medical images analysis, combining computer vision, radiomics and deep learning approaches. The team has a double