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and future developmental timepoints from time-series data. Our recent theoretical work suggests that these learned relationships can generalize across conditions, and we will test this using both
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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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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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amplification. The applicant will also apply and adapt these methods to various environmental samples and conditions, with special focus on consumer food products. Importantly, the successful applicant will be
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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