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tools, version control (Git), and collaborative coding (strong merit) Strong understanding of statistics and machine learning for high-dimensional data (strong merit) Experience with workflow management
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sequencing, and with computer scientists at KTH in Stockholm, focused on developing scalable probabilistic machine learning techniques for online phylogenomic analysis and placement of DNA barcodes. You will
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of Computing Science , Mathematics and Mathematical Statistics , Molecular Biology , Plant Physiology and Clinical Microbiology . More information regarding accessible research infrastructure can be found here
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model design and analysis as well as statistical model parametrization and validation techniques. This Postdoc position is part of a five-year research program funded by the Wallenberg Foundation, aimed
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skills in English Documented ability to independently drive research and publish scientific results Good ability to work in teams Personal suitability Merits: Research experience on Bayesian statistics and
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past
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evidenced by recent publications in e.g. Nature Biotechnology – and also provides a stimulating environment for learning computational biology. The successful applicant will in furthermore receive training
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society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read here