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to the continuous development of the Gaussian-Linear Hidden Markov Model ( GLHMM ) toolbox. In this role, you will be responsible for applying and validating statistical testing methodologies using datasets obtained
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on developing statistical and quantitative modeling approaches to genomic and phenomic data and apply these models for understanding the genetic mechanisms underlying variation in agronomic traits
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catalogue signature whistles for new populations, and implement statistical analysis for mark-recapture estimates of population density. The successful applicant will be based out of the Section for Marine
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of survey research, from planning and ethics approval, through pilot work and data collection, to statistical data analysis and write-up. Qualitative research skills and experience are an advantage but not
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documentation software, including git and Markdown. 6. Experience with statistical genetics approaches for functional genomics, including linkage disequilibrium score regression, cell type deconvolution and
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of science and issues of Global North-South disparities in research Strong background in computational social science and/or quantitative social science methods. Firm knowledge of conducting statistical
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processing, computer vision, computer simulation, numerical optimization, machine learning, computational modelling, geometry, and geometric statistics. The research work ranges from theoretical analyses, over