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generation sequencing Applying machine learning-guided directed evolution to improve multiple enzyme properties Upscaling selected biotransformation reactions in collaboration with academic partners
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modifications using high-resolution mass spectrometry and AI-based de novo peptide sequencing. Develop and apply machine learning models to predict protease activity and substrate specificity, integrating protein
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for identifying those sequences that fold correctly among a large starting library encoding disulfide-rich proteins. To develop the platform, we will focus primarily on sequences encoding animal venom
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explore large genomic datasets. Your tasks will include quantitative data analysis, sequence processing and evaluation, and pipeline development and automation. It is expected that you will lead the writing
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, and well-equipped working environment. The unit and the department have state-of-the-art facilities for next generation sequencing, single-cell transcriptomics, flow cytometry, mass spectrometry
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change, market dynamics, and daily grid variations. These factors contribute to heightened structural and control complexity, along with multiple layers of uncertainty. In this context, Hybrid Power Plants
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well as the organic matter persistence. Responsibilities and qualifications Your tasks will be to: Use bioinformatic tools to mine metagenomic datasets for enzyme-specific sequences (oxidoreductases and/or hydrolases
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PhD fellowship at the Copenhagen Center for Glycocalyx Research at the Department of Cellular and Mo
environment. Our research facilities include modern laboratories located in the Mærsk tower with access to state-of-the-art facilities for cell engineering, next-generation sequencing, flow cytometry, and mass