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sequencing data generation and interpretation Bioinformatics and statistical analysis, to characterize genome evolution, mutation processes, and adaptation patterns The PhD candidate will progressively
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sequencing data generation and interpretation Bioinformatics and statistical analysis, to characterize genome evolution, mutation processes, and adaptation patterns The PhD candidate will progressively
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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are applying for multiple projects, please submit a separate cover letter for each project. Your letter should include: A clear reference to the PhD project you're applying for (see available projects
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machine learning approaches to link gene-regulatory programs to neuronal phenotypes. Use explainable sequence-to-function models to interpret regulatory logic underlying neuronal identity and function