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for existing medications, a strategy that can significantly reduce the time and cost required to bring a drug to market. The project will use AI models such as CycleGANs, that by learning from complex spatial
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directions include: Quantitative genetics and phylogenetics: incorporating developmental constraints into evolutionary models and exploring how they shape patterns of variation. Modeling development from data
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in Python programming. Experience with machine learning methods, bioinformatics, and data science. Familiarity with generative AI tools for protein design and protein language models. Knowledge
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aims to build predictive and physical binding models of protein – DNA interactions using high-throughput and quantitative biochemical binding data across hundreds of thousands of sequence variants