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biology-experimental and/or theoretical biophysics-experimental and/or computational genomics-computer science, statistics, and/or machine learning with applications relevant to genomics-bioinformatics
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Computer/Information Sciences Internal Number: A-179059-11 General Description The Johns Hopkins University Data Science and AI (DSAI) Institute welcomes applications for its Postdoctoral Fellowship program
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
large data set types including RNAseq, DNAseq, RIPseq, and CLIPseq. Experience in gene expression analysis, alternative splicing analysis, machine learning, and motif analysis are preferred. Candidates
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assimilation, machine learning, and seasonal weather forecasts. As a Postdoctoral Research Fellow, you will play a crucial role in developing and testing statistical models for the accurate forecasting
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SPECIFICS Postdoctoral Scholar – Public Health Sciences Postdoctoral Scholar of Machine Learning and Statistical Genomics Description: The Department of Public Health Sciences is seeking postdoc scholars
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Posting Summary Logo Posting Number RTF00291PO25 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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terminal degree with deep knowledge of Artificial Intelligence and Machine Learning. Desired Qualifications Ability to help create scalable Artificial Intelligence workflows. Experience in working with
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to evaluate pancreatic cancer pathology using human tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming
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on intelligent observing systems using machine learning and data assimilation methods in the ACTIVATE project. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/289326
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Mattelaer, Christophe Ringeval). Research activities in include SM and BSM aspects of collider physics (LHC and future colliders, simulation tools, machine learning, effective field theories, amplitude