300 parallel-processing-bioinformatics "https:" positions at Pennsylvania State University
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. The postdoctoral fellow, under the mentorship of Dr. Dajiang Liu ( https://dajiangliu.blog ), is expected to work on at least one of these objectives. Requirements: A Ph.D. in statistics
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, genomics, bioinformatics, and biotechnology; investigates and evaluates research methods, advises faculty/researchers on optimal use of methodology and equipment; collects, correlates, analyzes
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students seeking hands-on experience in research, data science, bioinformatics, project coordination, and team-based scientific initiatives. Students selected for this opportunity may contribute to both
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, genomics, bioinformatics, and biotechnology; investigates and evaluates research methods, advises faculty/researchers on optimal use of methodology and equipment; collects, correlates, analyzes
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biology, bioinformatics, biological data analysis, and related quantitative approaches Analyze and interpret complex molecular and cellular datasets Collaborate with interdisciplinary teams across NCEMS
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genomics, quantitative genetics, bioinformatics, or a related field. Applicants must be able to provide evidence that all requirements have been met for completion of the Ph.D. prior to the effective date
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bioinformatics and computational biology tools and pipelines; supporting the publication of intermediate data products, code, and documentation; and developing and maintaining analysis pipelines compliant with
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integration of heterogeneous datasets to generate and test mechanistic biological hypotheses. Candidate Profile We are seeking candidates with a Ph.D., M.D., or equivalent doctoral degree in bioinformatics
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related fields by the start date Strong prior experience in large-scale calculations using CFD Strong skills in CFD for complex geometries, and parallel computing Development of advanced algorithms for
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Qualifications PhD in Bioinformatics, Computational Biology, Biology, Biomedical Sciences, Biostatistics, Computer Science, or related field Demonstrated experience analyzing high-dimensional