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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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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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in labour unions, etc. We seek a talented and open-minded candidate, who is eager to learn and has a genuine scientific interest. Extensive knowledge in and practical experience with protein expression
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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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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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and/or application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. Tasks The tasks include primarily leading and conducting research