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qualifications: Experience with genomic or transcriptomic data analysis, especially of plant species Basic understanding of genomics, evolution or population genetics theory Evidence of technical skills and
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social scientific data with ecological analysis from the “Blue Leads Green ” project. About the position The PhD student will have the freedom to co-design their project that may explore questions related
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quantitative and population genetics; good knowledge and practical skills in genetic and genomic analysis (both, marker data and sequence data), phenology and plant pathology; a sound understanding of
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. The candidate should have strong mathematical skills and experience in machine learning, programming, and the analysis of visual data. Furthermore, you are able to work with a long-term perspective and drive
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the infrared spectral region experience with programming for data analysis in Python and experimental control in LabVIEW ability to work both independently and collaboratively with other researchers Additional
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diagnostics covers data integration, analysis, visualization, and data interpretation for patient stratification, discovery of biomarkers for disease risks, diagnosis, drug response and monitoring of health
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well as the efficacy of the live rotavirus vaccines. In this project we will combine evolutionary analyses, historical data, and functional organoid studies in a multidisciplinary approach. By performing analysis
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graduate education in more than 80 subjects. At the Department of Clinical Microbiology, Umeå University, we are announcing the position as a Data driven life science doctoral student in Data-driven
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fecal shotgun data from 500 children followed from birth to three years of age to investigate how environmental, clinical and lifestyle exposures shape microbial community development. The student will be
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languages. Have experience of quantitative analysis, computational modelling, bioinformatics, machine learning, or another form of data-intensive academic research. Have a strong interest in interdisciplinary