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. About the Role The post is funded for 3 years and is based in the Big Data Institute, Old Road Campus. You will join an interdisciplinary team of researchers spanning imaging science, machine learning
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science, has fundamentally changed the scope and ambition of harnessing the potential of biological systems. Big data approaches and analysis of biological systems are key research instruments at the Center
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, which involves building a large corpus of Latin texts (data collection and pre-processing), mining dictionary data, and developing novel algorithms for time-sensitive word sense disambiguation (WSD) in
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, and computational humanities. The postholder will lead the curated stream of the project, which involves designing a large corpus of Latin texts, curating it (correction of pre-processed data and corpus
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increasing independence over time. Collaborate on project and analysis design guided by their PI. Develop new computational methods. Adhere to field and lab standards for data analysis. Identify, process
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performance. Position 1: Postdoctoral Research Associate – Detector Design & Analysis Qualifications: PhD in Experimental Particle Physics or related field. Preferred Qualifications Physics analysis
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opportunities to work with large-scale whole genome and whole exome sequencing, RNA-seq, ATAC-seq, proteomics, and metabolomics data in a well-established cohort of childhood cancer survivors with clinically
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Alzheimer’s disease to develop and/or apply computational approaches to large scale genomic or transcriptomic datasets for identification of targets for early detection, prevention or treatment of Alzheimer’s
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
pending funding and performance. Minimum Education and Experience Requirements PhD in Computer Science, Computing, Statistics or Data Science, or related disciplines Required Qualifications, Competencies
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qualification/experience equivalent to PhD level in health data science or similar field. Experience working within multidisciplinary teams and in the preparation and analysis of large healthcare datasets such as