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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