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“Quantitative predictions of protein – DNA interactions from high-throughput biophysical binding data”. Sequence specific binding and recognition between transcription factors and DNA control gene expression at
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to contribute with creative and innovative technical solutions to detect metabolites and lipids from biological samples, including individual cells and thin tissue sections, with mass spectrometry. Tasks may
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biology of infection covers research that will transform our understanding of pathogens, their interactions with hosts and the environment, and how they are transmitted through populations. Research will
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year project, funded by the DDLS program, we aim to develop AI-based tools in design of affinity ligands, such as the prediction of binding interactions between proteins. Data-driven life science (DDLS
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of the thesis project, written and oral proficiency in English, the capacity for analytical thinking, the ability to collaborate, as well as creativity, initiative, and independence. The assessment will be based
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focuses on the development of novel fluorinated amino acids as a 19F-NMR probe to study protein dynamics and protein-ligand interaction to facilitate drug discovery. It is financed by SciLifeLab, and hence
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transform our understanding of pathogens, their interactions with hosts and the environment, and how they are transmitted through populations. Research will have a strong focus on computational analysis
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. Previous experience with next-generation sequencing data, deep learning or RNA biology are advantages, but not required. capacity for analytical and creative thinking initiative independence ability