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of the proteins involved in the project, but also applying machine learning to predict the effects of allosteric modulation and to understand the biology of the specific systems we are studying. Qualification
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how developmental dynamics can both open up and restrict evolutionary possibilities, and how this knowledge can help us better understand, predict, and even influence evolutionary change. We approach
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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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on developing strategies for personalized prevention, individualized risk prediction, early detection, and tailored therapeutic and prognostic tools for pancreatic cancer. The team currently includes
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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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gene expression profiles and cellular heterogeneity within tissues can predict how existing drugs might act on previously uncharacterized disease mechanisms or cellular subtypes. These models will be
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time and space and create AI-based models to predict human cells. As the Scientific Program Manager, you will make a key contribution to this exciting new direction at SciLifeLab through setting up
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Cell initiative is a new flagship research program aiming to develop an AI model of a human cell to predict key cellular functions. It is funded by the Knut & Alice Wallenberg Foundation (KAW) and
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new flagship research program aiming to to map the molecular structure and function of single human cells in time and space and create AI-based models to predict human cells. It is funded by the Knut